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model (94)
use (79)
approach (60)

Stem bayesian$ (all stems)

508 papers:

CASECASE-2015-SabbaghiHD #3d #modelling #quality
Bayesian additive modeling for quality control of 3D printed products (AS, QH, TD), pp. 906–911.
CASECASE-2015-SterlingSZC #algorithm #optimisation #parametricity #process
Welding parameter optimization based on Gaussian process regression Bayesian optimization algorithm (DS, TS, YZ, HC), pp. 1490–1496.
DACDAC-2015-HuangFYZL #estimation #multi #performance
Efficient multivariate moment estimation via Bayesian model fusion for analog and mixed-signal circuits (QH, CF, FY, XZ, XL), p. 6.
DATEDATE-2015-FangHYZLG #estimation #fault #performance
Efficient bit error rate estimation for high-speed link by Bayesian model fusion (CF, QH, FY, XZ, XL, CG), pp. 1024–1029.
DATEDATE-2015-LiaperdosSATAL #deployment #performance #using
Fast deployment of alternate analog test using Bayesian model fusion (JL, HGDS, LA, YT, AA, XL), pp. 1030–1035.
SIGMODSIGMOD-2015-YangSN #correlation #difference #privacy
Bayesian Differential Privacy on Correlated Data (BY, IS, HN), pp. 747–762.
ICMLICML-2015-BenavoliCMZ #algorithm #parametricity
A Bayesian nonparametric procedure for comparing algorithms (AB, GC, FM, MZ), pp. 1264–1272.
ICMLICML-2015-DasBB #modelling #order #parametricity
Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models (MKD, TB, CB), pp. 550–559.
ICMLICML-2015-Hernandez-Lobato15a #constraints #optimisation #predict
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints (JMHL, MAG, MWH, RPA, ZG), pp. 1699–1707.
ICMLICML-2015-Hernandez-Lobato15b #learning #network #probability #scalability
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks (JMHL, RA), pp. 1861–1869.
ICMLICML-2015-HugginsT
Risk and Regret of Hierarchical Bayesian Learners (JH, JT), pp. 1442–1451.
ICMLICML-2015-KandasamySP #modelling #optimisation
High Dimensional Bayesian Optimisation and Bandits via Additive Models (KK, JGS, BP), pp. 295–304.
ICMLICML-2015-KusnerGGW #optimisation
Differentially Private Bayesian Optimization (MJK, JRG, RG, KQW), pp. 918–927.
ICMLICML-2015-MatthewCYW #empirical
Bayesian and Empirical Bayesian Forests (TM, CSC, JY, MW), pp. 967–976.
ICMLICML-2015-RajanHSFJ #locality #multi
Bayesian Multiple Target Localization (PR, WH, RS, PIF, BJ), pp. 1945–1953.
ICMLICML-2015-SamoR #parametricity #process #scalability
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes (YLKS, SR), pp. 2227–2236.
ICMLICML-2015-SnoekRSKSSPPA #network #optimisation #scalability #using
Scalable Bayesian Optimization Using Deep Neural Networks (JS, OR, KS, RK, NS, NS, MMAP, P, RPA), pp. 2171–2180.
ICMLICML-2015-Suzuki #convergence
Convergence rate of Bayesian tensor estimator and its minimax optimality (TS), pp. 1273–1282.
ICMLICML-2015-WangZ #clustering #named #parametricity
DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics (YW, JZ), pp. 862–870.
ICMLICML-2015-ZhaoMP #network #on the
On the Relationship between Sum-Product Networks and Bayesian Networks (HZ, MM, PP), pp. 116–124.
KDDKDD-2015-AhnKLRW #distributed #matrix #probability #scalability #using
Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC (SA, AK, NL, SR, MW), pp. 9–18.
KDDKDD-2015-ScheinPBW #multi
Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts (AS, JWP, DMB, HMW), pp. 1045–1054.
KDDKDD-2015-ZhangW #recommendation
A Collective Bayesian Poisson Factorization Model for Cold-start Local Event Recommendation (WZ, JW), pp. 1455–1464.
MLDMMLDM-2015-KrasotkinaM #approach #optimisation #ranking
A Bayesian Approach to Sparse Learning-to-Rank for Search Engine Optimization (OK, VM), pp. 382–394.
MLDMMLDM-2015-KrasotkinaM15a #analysis #approach
A Bayesian Approach to Sparse Cox Regression in High-Dimentional Survival Analysis (OK, VM), pp. 425–437.
RecSysRecSys-2015-GuoD #approach #bias #category theory
Uncovering Systematic Bias in Ratings across Categories: a Bayesian Approach (FG, DBD), pp. 317–320.
SIGIRSIGIR-2015-GrotovWR #comparison #interactive
Bayesian Ranker Comparison Based on Historical User Interactions (AG, SW, MdR), pp. 273–282.
MoDELSMoDELS-2015-LuddeckeSSS #modelling #network #using
Modeling user intentions for in-car infotainment systems using Bayesian networks (DL, CS, JS, IS), pp. 378–385.
OOPSLAOOPSLA-2015-OhYY #adaptation #learning #optimisation #program analysis
Learning a strategy for adapting a program analysis via bayesian optimisation (HO, HY, KY), pp. 572–588.
SACSAC-2015-Grossl #modelling #network
Modeling dependable systems with continuous time Bayesian networks (MG), pp. 436–441.
SACSAC-2015-JeongYAYP #algorithm #interactive #network #search-based #using
Inference of disease-specific gene interaction network using a Bayesian network learned by genetic algorithm (DJ, YY, JA, YY, SP), pp. 47–53.
SACSAC-2015-PerkusichMSGAP #approach #metric #network
A Bayesian network approach to assist on the interpretation of software metrics (MP, AM, LCeS, KCG, HOdA, AP), pp. 1498–1503.
DACDAC-2014-FangYZL #estimation #named #performance
BMF-BD: Bayesian Model Fusion on Bernoulli Distribution for Efficient Yield Estimation of Integrated Circuits (CF, FY, XZ, XL), p. 6.
DACDAC-2014-YuSHEAB #metric #parametricity #using
Remembrance of Transistors Past: Compact Model Parameter Extraction Using Bayesian Inference and Incomplete New Measurements (LY, SS, CH, IAME, DAA, DSB), p. 6.
SIGMODSIGMOD-2014-ZhangCPSX #named #network
PrivBayes: private data release via bayesian networks (JZ, GC, CMP, DS, XX), pp. 1423–1434.
HCIDHM-2014-ZhangGBD #industrial #network
Application of Bayesian Networks in Consumer Service Industry and Healthcare (LZ, YG, BB, VGD), pp. 484–495.
ICEISICEIS-v2-2014-LiuDT #network #reliability
Auditing Data Reliability in International Logistics — An Application of Bayesian Networks (LL, HAMD, RT), pp. 707–712.
ICMLICML-c1-2014-LacosteMLL #learning
Agnostic Bayesian Learning of Ensembles (AL, MM, FL, HL), pp. 611–619.
ICMLICML-c1-2014-MeiZZ #first-order #logic #modelling #robust
Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian Models (SM, JZ, JZ), pp. 253–261.
ICMLICML-c1-2014-NguyenPNVB #clustering #multi #parametricity
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts (TVN, DQP, XN, SV, HB), pp. 288–296.
ICMLICML-c1-2014-ShiZ #learning #online
Online Bayesian Passive-Aggressive Learning (TS, JZ), pp. 378–386.
ICMLICML-c2-2014-BaiLS #classification #framework #online
A Bayesian Framework for Online Classifier Ensemble (QB, HL, SS), pp. 1584–1592.
ICMLICML-c2-2014-BarberW #difference #equation #estimation #process
Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations (DB, YW), pp. 1485–1493.
ICMLICML-c2-2014-BenavoliCMZR #process
A Bayesian Wilcoxon signed-rank test based on the Dirichlet process (AB, GC, FM, MZ, FR), pp. 1026–1034.
ICMLICML-c2-2014-Chapados #effectiveness #modelling
Effective Bayesian Modeling of Groups of Related Count Time Series (NC), pp. 1395–1403.
ICMLICML-c2-2014-GardnerKZWC #constraints #difference #optimisation
Bayesian Optimization with Inequality Constraints (JRG, MJK, ZEX, KQW, JC), pp. 937–945.
ICMLICML-c2-2014-JohnsonW #modelling #probability
Stochastic Variational Inference for Bayesian Time Series Models (MJ, ASW), pp. 1854–1862.
ICMLICML-c2-2014-LiZ0 #learning #multi
Bayesian Max-margin Multi-Task Learning with Data Augmentation (CL, JZ, JC), pp. 415–423.
ICMLICML-c2-2014-MinskerSLD #robust #scalability
Scalable and Robust Bayesian Inference via the Median Posterior (SM, SS, LL, DBD), pp. 1656–1664.
ICMLICML-c2-2014-PentinaL #bound #learning
A PAC-Bayesian bound for Lifelong Learning (AP, CHL), pp. 991–999.
ICMLICML-c2-2014-RaiWGCDC #composition #multi #rank #scalability
Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors (PR, YW, SG, GC, DBD, LC), pp. 1800–1808.
ICMLICML-c2-2014-SnoekSZA #optimisation
Input Warping for Bayesian Optimization of Non-Stationary Functions (JS, KS, RSZ, RPA), pp. 1674–1682.
ICPRICPR-2014-ChamroukhiBG #clustering #parametricity
Bayesian Non-parametric Parsimonious Gaussian Mixture for Clustering (FC, MB, HG), pp. 1460–1465.
ICPRICPR-2014-ChironGM #behaviour #parametricity #using
Discovering Emergent Behaviors from Tracks Using Hierarchical Non-parametric Bayesian Methods (GC, PGK, MM), pp. 2185–2190.
ICPRICPR-2014-Filippone #classification #process #pseudo
Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMC (MF), pp. 614–619.
ICPRICPR-2014-KumarG #documentation #keyword #learning
Bayesian Active Learning for Keyword Spotting in Handwritten Documents (GK, VG), pp. 2041–2046.
ICPRICPR-2014-KunwarPB #network #online #recognition
Semi-supervised Online Bayesian Network Learner for Handwritten Characters Recognition (RK, UP, MB), pp. 3104–3109.
ICPRICPR-2014-LiRPV #modelling #topic #using
Regularizing Topic Discovery in EMRs with Side Information by Using Hierarchical Bayesian Models (CL, SR, DQP, SV), pp. 1307–1312.
ICPRICPR-2014-NguyenGVP #framework #parametricity #process #recognition #using
A Bayesian Nonparametric Framework for Activity Recognition Using Accelerometer Data (TN, SKG, SV, DQP), pp. 2017–2022.
ICPRICPR-2014-NieJ #learning #linear #using
Feature Learning Using Bayesian Linear Regression Model (SN, QJ), pp. 1502–1507.
ICPRICPR-2014-OHarneyMRCSCBF #kernel #learning #multi #pseudo
Pseudo-Marginal Bayesian Multiple-Class Multiple-Kernel Learning for Neuroimaging Data (ADO, AM, KR, KC, ABS, AC, CB, MF), pp. 3185–3190.
ICPRICPR-2014-OsogamiK
A Hierarchical Bayesian Choice Model with Visibility (TO, TK), pp. 3618–3623.
ICPRICPR-2014-QuLWXT #framework #robust #set
Robust Point Set Matching under Variational Bayesian Framework (HBQ, JCL, JQW, LX, HJT), pp. 58–63.
ICPRICPR-2014-XingY #categorisation #image #parametricity #representation #scalability
Large Scale Image Categorization in Sparse Nonparametric Bayesian Representation (SX, NHCY), pp. 1365–1370.
ICPRICPR-2014-XuS #learning #network #using
Bayesian Network Structure Learning Using Causality (ZX, SNS), pp. 3546–3551.
ICPRICPR-2014-YamadaM #approach #behaviour #using
Estimating Driver Awareness of Crossing Pedestrians While Turning Left Based on Vehicle Behavior Using Bayesian Approach (KY, TM), pp. 1898–1903.
KDDKDD-2014-EmbarPB #framework #network
A bayesian framework for estimating properties of network diffusions (VRE, RKP, IB), pp. 1216–1225.
KDDKDD-2014-VasishtDVK #classification #learning #multi
Active learning for sparse bayesian multilabel classification (DV, ACD, MV, AK), pp. 472–481.
MLDMMLDM-2014-FuMD #classification #multi #network #performance #towards
Towards the Efficient Recovery of General Multi-Dimensional Bayesian Network Classifier (SF, SM, MCD), pp. 16–30.
MLDMMLDM-2014-KhasnabishSDS #detection #learning #programming language #source code #using
Detecting Programming Language from Source Code Using Bayesian Learning Techniques (JNK, MS, JD, GS), pp. 513–522.
MLDMMLDM-2014-SandovalH #learning #network #using
Learning of Natural Trading Strategies on Foreign Exchange High-Frequency Market Data Using Dynamic Bayesian Networks (JS, GH), pp. 408–421.
RecSysRecSys-2014-KimC #collaboration #predict
Bayesian binomial mixture model for collaborative prediction with non-random missing data (YDK, SC), pp. 201–208.
RecSysRecSys-2014-LercheJ #feedback #personalisation #ranking #using
Using graded implicit feedback for bayesian personalized ranking (LL, DJ), pp. 353–356.
SIGIRSIGIR-2014-CaiLR #documentation #matrix #personalisation #probability #ranking
Personalized document re-ranking based on Bayesian probabilistic matrix factorization (FC, SL, MdR), pp. 835–838.
SACSAC-2014-SeffrinRJ #algebra #network
A dynamic bayesian network for inference of learners’ algebraic knowledge (HMS, GLR, PAJ), pp. 235–240.
IJCARIJCAR-2014-CeylanP #logic
The Bayesian Description Logic ℬℰℒ (IIC, RP), pp. 480–494.
CASECASE-2013-LinG #detection #framework #network #optimisation #synthesis
Synthesis and optimization of a Bayesian belief network based observation platform for anomaly detection under partial and unreliable observations (WCL, HEG), pp. 51–58.
DACDAC-2013-GuCL #estimation #performance #validation
Efficient moment estimation with extremely small sample size via bayesian inference for analog/mixed-signal validation (CG, EC, XL), p. 7.
DACDAC-2013-WangZSLG #modelling #performance #reuse #scalability
Bayesian model fusion: large-scale performance modeling of analog and mixed-signal circuits by reusing early-stage data (FW, WZ, SS, XL, CG), p. 6.
ICDARICDAR-2013-PuriST #learning #network
Bayesian Network Structure Learning and Inference Methods for Handwriting (MP, SNS, YT), pp. 1320–1324.
ICDARICDAR-2013-RamaiahSG #framework #modelling
A Bayesian Framework for Modeling Accents in Handwriting (CR, AS, VG), pp. 917–921.
SIGMODSIGMOD-2013-LinK #estimation #privacy #statistics
Information preservation in statistical privacy and bayesian estimation of unattributed histograms (BRL, DK), pp. 677–688.
HCIDHM-SET-2013-BusogiKSRYK #behaviour #problem
Bayesian Affordance-Based Agent Model for Wayfinding Behaviors in Evacuation Problems (MB, NK, DS, HBR, AY, DK), pp. 297–306.
ICEISICEIS-J-2013-LiL13a #object-oriented #predict
Bayesian Prediction of Fault-Proneness of Agile-Developed Object-Oriented System (LL, HL), pp. 209–225.
ICEISICEIS-v2-2013-LiL #agile #network #object-oriented #predict #process #using
Predicting Fault-proneness of Object-Oriented System Developed with Agile Process using Learned Bayesian Network (LL, HL), pp. 5–16.
ICMLICML-c1-2013-AnandkumarHJK #learning #linear #network
Learning Linear Bayesian Networks with Latent Variables (AA, DH, AJ, SK), pp. 249–257.
ICMLICML-c2-2013-WenKEB
Sequential Bayesian Search (ZW, BK, BE, SB), pp. 226–234.
ICMLICML-c3-2013-BellemareVB #learning #recursion
Bayesian Learning of Recursively Factored Environments (MGB, JV, MB), pp. 1211–1219.
ICMLICML-c3-2013-GermainHLM #adaptation #approach #classification #linear
A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers (PG, AH, FL, EM), pp. 738–746.
ICMLICML-c3-2013-GonenKK #kernel #matrix
Kernelized Bayesian Matrix Factorization (MG, SAK, SK), pp. 864–872.
ICMLICML-c3-2013-GrosshansSBS #game studies #problem
Bayesian Games for Adversarial Regression Problems (MG, CS, MB, TS), pp. 55–63.
ICMLICML-c3-2013-GuptaPV #approach #learning #multi #parametricity
Factorial Multi-Task Learning : A Bayesian Nonparametric Approach (SKG, DQP, SV), pp. 657–665.
ICMLICML-c3-2013-LakshminarayananRT #top-down
Top-down particle filtering for Bayesian decision trees (BL, DMR, YWT), pp. 280–288.
ICMLICML-c3-2013-PeharzTP #generative #network
The Most Generative Maximum Margin Bayesian Networks (RP, ST, FP), pp. 235–243.
KDDKDD-2013-AhmedS #modelling #parametricity #scalability
The dataminer’s guide to scalable mixed-membership and nonparametric bayesian models (AA, AJS), p. 1529.
KDDKDD-2013-FouldsBDSW #probability
Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation (JRF, LB, CD, PS, MW), pp. 446–454.
KDDKDD-2013-NaganoITUA #modelling #parametricity
Nonparametric hierarchal bayesian modeling in non-contractual heterogeneous survival data (SN, YI, NT, TU, MA), pp. 668–676.
RecSysRecSys-2013-BabasCT #personalisation #recommendation #what
You are what you consume: a bayesian method for personalized recommendations (KB, GC, ET), pp. 221–228.
SEKESEKE-2013-DuttaAKB #approach #distributed
Virtual Medical Board: A Distributed Bayesian Agent Based Approach (S) (AD, SA, AK, SB), pp. 685–688.
POPLPOPL-2013-GordonABCGNRR #reasoning
A model-learner pattern for bayesian reasoning (ADG, MA, JB, GC, TG, AVN, SKR, CVR), pp. 403–416.
ESEC-FSEESEC-FSE-2013-ClaretRNGB #analysis #data flow #using
Bayesian inference using data flow analysis (GC, SKR, AVN, ADG, JB), pp. 92–102.
CASECASE-2012-YamaguchiIS #data-driven #database #fault #network
Data based construction of Bayesian Network for fault diagnosis of event-driven systems (TY, SI, TS), pp. 508–514.
VLDBVLDB-2012-SatuluriP #locality #performance #similarity
Bayesian Locality Sensitive Hashing for Fast Similarity Search (VS, SP), pp. 430–441.
VLDBVLDB-2012-ZhaoRGH #approach #integration
A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration (BZ, BIPR, JG, JH), pp. 550–561.
STOCSTOC-2012-BeiCGL #design
Budget feasible mechanism design: from prior-free to bayesian (XB, NC, NG, PL), pp. 449–458.
ICEISICEIS-v1-2012-NikovskiEYST #automation #composition #network
Bayesian Networks for Matcher Composition in Automatic Schema Matching (DN, AE, XY, MS, ST), pp. 48–55.
CIKMCIKM-2012-JiaZH #network #simulation
Non-stationary bayesian networks based on perfect simulation (YJ, WZ, JH), pp. 1095–1104.
CIKMCIKM-2012-XuZG #multi #online
Multiview hierarchical bayesian regression model andapplication to online advertising (TX, RZ, ZG), pp. 485–494.
ICMLICML-2012-AhnBW #probability
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring (SA, AKB, MW), p. 230.
ICMLICML-2012-AzimiJF #hybrid #optimisation
Hybrid Batch Bayesian Optimization (JA, AJ, XZF), p. 45.
ICMLICML-2012-BachrachGMG #adaptation #crowdsourcing #how #testing #visual notation
How To Grade a Test Without Knowing the Answers — A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing (YB, TG, TM, JG), p. 108.
ICMLICML-2012-BorboudakisT #constraints #graph #information management #network
Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs (GB, IT), p. 59.
ICMLICML-2012-BracegirdleB
Bayesian Conditional Cointegration (CB, DB), p. 220.
ICMLICML-2012-DundarAQR #learning #modelling #online
Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes (MD, FA, AQ, BR), p. 18.
ICMLICML-2012-FujimakiH #markov #modelling
Factorized Asymptotic Bayesian Hidden Markov Models (RF, KH), p. 157.
ICMLICML-2012-GarnettKXSM
Bayesian Optimal Active Search and Surveying (RG, YK, XX, JGS, RPM), p. 111.
ICMLICML-2012-Gonen #kernel #learning #multi #performance
Bayesian Efficient Multiple Kernel Learning (MG), p. 17.
ICMLICML-2012-KoS #modelling #scalability
Large Scale Variational Bayesian Inference for Structured Scale Mixture Models (YJK, MWS), p. 229.
ICMLICML-2012-KulisJ #algorithm
Revisiting k-means: New Algorithms via Bayesian Nonparametrics (BK, MIJ), p. 148.
ICMLICML-2012-MohamedHG #learning
Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning (SM, KAH, ZG), p. 91.
ICMLICML-2012-MorvantKR #bound #classification #matrix #multi
PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification (EM, SK, LR), p. 158.
ICMLICML-2012-PaisleyBJ #probability
Variational Bayesian Inference with Stochastic Search (JWP, DMB, MIJ), p. 177.
ICMLICML-2012-PeharzP #learning #network
Exact Maximum Margin Structure Learning of Bayesian Networks (RP, FP), p. 102.
ICMLICML-2012-ShterevD
Bayesian Watermark Attacks (IS, DBD), p. 153.
ICMLICML-2012-WangWHL #learning #monte carlo
Monte Carlo Bayesian Reinforcement Learning (YW, KSW, DH, WSL), p. 105.
ICMLICML-2012-XuYQ #composition #data analysis #infinity #modelling #multi #parametricity
Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis (ZX, FY, AQ), p. 218.
ICMLICML-2012-ZhongG #approach #approximate #matrix
A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices (MZ, MAG), p. 87.
ICPRICPR-2012-Capitaine #equivalence #probability
Set-valued Bayesian inference with probabilistic equivalence (HLC), pp. 2132–2135.
ICPRICPR-2012-GuK #learning #online #visual notation
Grassmann manifold online learning and partial occlusion handling for visual object tracking under Bayesian formulation (IYHG, ZHK), pp. 1463–1466.
ICPRICPR-2012-GuptaPV #parametricity
A nonparametric Bayesian Poisson gamma model for count data (SKG, DQP, SV), pp. 1815–1818.
ICPRICPR-2012-KafaiBA #classification #clustering #estimation #network
Cluster-Classification Bayesian Networks for head pose estimation (MK, BB, LA), pp. 2869–2872.
ICPRICPR-2012-TangS #independence #learning #network #performance #testing #using
Efficient and accurate learning of Bayesian networks using chi-squared independence tests (YT, SNS), pp. 2723–2726.
ICPRICPR-2012-TianC #image #video
Bayesian image enlargement for mixed-resolution video (JT, LC), pp. 3082–3085.
ICPRICPR-2012-TurkovKM #concept #pattern matching #pattern recognition #problem #recognition
The Bayesian logistic regression in pattern recognition problems under concept drift (PAT, OK, VM), pp. 2976–2979.
ICPRICPR-2012-WangJ #network #recognition
Incorporating contextual knowledge to Dynamic Bayesian Networks for event recognition (XW, QJ), pp. 3378–3381.
ICPRICPR-2012-WangJ12b #learning #network #process #recognition
Learning dynamic Bayesian network discriminatively for human activity recognition (XW, QJ), pp. 3553–3556.
ICPRICPR-2012-YoonFW #generative
Bayesian separation of wind power generation signals (JWY, FF, MW), pp. 2660–2663.
ICPRICPR-2012-YoonT #implementation
Bayesian implementation of a Lagrangian macroscopic traffic flow model (JWY, TTT), pp. 214–217.
ICPRICPR-2012-ZhangWN #detection #feature model #student
Bayesian feature selection and model detection for student’s t-mixture distributions (HZ, QMJW, TMN), pp. 1631–1634.
ICPRICPR-2012-ZhaoXY #learning #network #speech
Unsupervised Tibetan speech features Learning based on Dynamic Bayesian Networks (YZ, XX, GY), pp. 2319–2322.
KDDKDD-2012-SilvaC #learning #matrix #online
Active learning for online bayesian matrix factorization (JGS, LC), pp. 325–333.
KDDKDD-2012-Ueda #data analysis #relational
Bayesian relational data analysis (NU), p. 815.
KDIRKDIR-2012-AlkouzA #approach #network #semantics #social
An Interests Discovery Approach in Social Networks based on a Semantically Enriched Bayesian Network Model (AA, SA), pp. 300–305.
KDIRKDIR-2012-CheungZZ #approach #network
A Bayesian Approach for Constructing Ensemble Neural Network (SHC, YZ, ZZ), pp. 374–377.
KMISKMIS-2012-KataokaTKH #information management #network
An Information Sharing Method for Skilled Management Operations based on Bayesian Network Inference (TK, KT, MK, MH), pp. 257–260.
MLDMMLDM-2012-BouhamedMLR #heuristic #learning #network
A New Learning Structure Heuristic of Bayesian Networks from Data (HB, AM, TL, AR), pp. 183–197.
MLDMMLDM-2012-TurkovKM #approach #concept #pattern matching #pattern recognition #problem #recognition
Bayesian Approach to the Concept Drift in the Pattern Recognition Problems (PAT, OK, VM), pp. 1–10.
RecSysRecSys-2012-Herbrich #distributed #learning #online #realtime
Distributed, real-time bayesian learning in online services (RH), pp. 203–204.
SEKESEKE-2012-PittoliSN #monitoring #network
Investigating the Use of Bayesian Networks as a Support Tool for Monitoring Software Projects (FP, ALRdS, DJN), pp. 570–573.
SIGIRSIGIR-2012-NunzioS #classification #data analysis #naive bayes #visual notation
A visual tool for bayesian data analysis: the impact of smoothing on naive bayes text classifiers (GMDN, AS), p. 1002.
SACSAC-2012-JabeurTB #microblog #network #retrieval #twitter
Uprising microblogs: a bayesian network retrieval model for tweet search (LBJ, LT, MB), pp. 943–948.
CBSECBSE-2011-AletiM #component #deployment #learning #optimisation
Component deployment optimisation with bayesian learning (AA, IM), pp. 11–20.
CASECASE-2011-KurzKP #maintenance #network #using
Dynamic Maintenance in semiconductor manufacturing using Bayesian networks (DK, JK, JP), pp. 238–243.
DACDAC-2011-WangXAP #classification #learning #policy #power management #using
Deriving a near-optimal power management policy using model-free reinforcement learning and Bayesian classification (YW, QX, ACA, MP), pp. 41–46.
ICDARICDAR-2011-EmilieBA #constraints #network #physics #recognition #semantics
Use of Semantic and Physical Constraints in Bayesian Networks for Form Recognition (EP, YB, AB), pp. 946–950.
ICDARICDAR-2011-ShahabSD #approach #recognition
Bayesian Approach to Photo Time-Stamp Recognition (AS, FS, AD), pp. 1039–1043.
ESOPESOP-2011-BorgstromGGMG #machine learning #semantics
Measure Transformer Semantics for Bayesian Machine Learning (JB, ADG, MG, JM, JVG), pp. 77–96.
HCIDHM-2011-EilersM #composition #learning #modelling #using
Learning the Relevant Percepts of Modular Hierarchical Bayesian Driver Models Using a Bayesian Information Criterion (ME, CM), pp. 463–472.
HCIDHM-2011-MobusEG #composition #predict
Predicting the Focus of Attention and Deficits in Situation Awareness with a Modular Hierarchical Bayesian Driver Model (CM, ME, HG), pp. 483–492.
ICEISICEIS-v2-2011-ZhuXM #assessment #network #risk management #using
Operational Hazard Risk Assessment using Bayesian Networks (ZJZ, YX, EM), pp. 135–139.
ICEISICEIS-v4-2011-HuGK #evaluation #modelling #network #trust
Supply Chain Trust Evaluation Model based on Bayesian Network (XH, FG, YK), pp. 712–715.
CIKMCIKM-2011-HarveyCRC #collaboration #modelling #predict #rating
Bayesian latent variable models for collaborative item rating prediction (MH, MJC, IR, FC), pp. 699–708.
CIKMCIKM-2011-TomasevRMI #approach #classification #nearest neighbour #probability
A probabilistic approach to nearest-neighbor classification: naive hubness bayesian kNN (NT, MR, DM, MI), pp. 2173–2176.
CIKMCIKM-2011-WangL #framework #learning #named #rank
CoRankBayes: bayesian learning to rank under the co-training framework and its application in keyphrase extraction (CW, SL), pp. 2241–2244.
ICMLICML-2011-BarthelmeC #named
ABC-EP: Expectation Propagation for Likelihoodfree Bayesian Computation (SB, NC), pp. 289–296.
ICMLICML-2011-DoshiWTR #infinity #network
Infinite Dynamic Bayesian Networks (FD, DW, JBT, NR), pp. 913–920.
ICMLICML-2011-NakajimaSB #automation #on the
On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution (SN, MS, SDB), pp. 497–504.
ICMLICML-2011-NikolenkoS #contest #rating
A New Bayesian Rating System for Team Competitions (SIN, AS), pp. 601–608.
ICMLICML-2011-VirtanenKK
Bayesian CCA via Group Sparsity (SV, AK, SK), pp. 457–464.
ICMLICML-2011-WellingT #learning #probability
Bayesian Learning via Stochastic Gradient Langevin Dynamics (MW, YWT), pp. 681–688.
KDDKDD-2011-HuangLYFCWR #effectiveness #modelling #network
Brain effective connectivity modeling for alzheimer’s disease by sparse gaussian bayesian network (SH, JL, JY, AF, KC, TW, ER), pp. 931–939.
RecSysRecSys-2011-BarbieriCMO #approach #modelling #recommendation
Modeling item selection and relevance for accurate recommendations: a bayesian approach (NB, GC, GM, RO), pp. 21–28.
SACSAC-2011-YeHL #approach #network #query
A Bayesian network approach to context sensitive query expansion (ZY, XH, HL), pp. 1138–1142.
ICLPICLP-2011-ChristiansenHLP #analysis #network #sequence
Bayesian Annotation Networks for Complex Sequence Analysis (HC, CTH, OTL, MP), pp. 220–230.
DACDAC-2010-ZhangLR
Bayesian virtual probe: minimizing variation characterization cost for nanoscale IC technologies via Bayesian inference (WZ, XL, RAR), pp. 262–267.
DATEDATE-2010-KrishnanDBK
Block-level bayesian diagnosis of analogue electronic circuits (SK, KDD, RB, HGK), pp. 1767–1772.
STOCSTOC-2010-HartlineL #algorithm #design
Bayesian algorithmic mechanism design (JDH, BL), pp. 301–310.
IFLIFL-2010-TorontoM #probability
From Bayesian Notation to Pure Racket via Discrete Measure-Theoretic Probability in λ ZFC (NT, JM), pp. 89–104.
ICEISICEIS-AIDSS-2010-AhdabG #learning #network #performance
Efficient Learning of Dynamic Bayesian Networks from Timed Data (AA, MLG), pp. 226–231.
ICEISICEIS-AIDSS-2010-TroianoP #complexity #modelling
Supporting Complexity in Modeling Bayesian Troubleshooting (LT, DDP), pp. 344–349.
CIKMCIKM-2010-ZhangWWCZHZ #learning #modelling
Learning click models via probit bayesian inference (YZ, DW, GW, WC, ZZ, BH, LZ), pp. 439–448.
ICMLICML-2010-DondelingerLH #flexibility #information management #network
Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing (FD, SL, DH), pp. 303–310.
ICMLICML-2010-DowneyS #adaptation #difference
Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting λ (CD, SS), pp. 311–318.
ICMLICML-2010-GraepelCBH #predict
Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft’s Bing Search Engine (TG, JQC, TB, RH), pp. 13–20.
ICMLICML-2010-HoffmanBC #matrix #music #parametricity
Bayesian Nonparametric Matrix Factorization for Recorded Music (MDH, DMB, PRC), pp. 439–446.
ICMLICML-2010-LazaricG #learning #multi
Bayesian Multi-Task Reinforcement Learning (AL, MG), pp. 599–606.
ICMLICML-2010-LiangJK #approach #learning #source code
Learning Programs: A Hierarchical Bayesian Approach (PL, MIJ, DK), pp. 639–646.
ICMLICML-2010-NakajimaS #matrix
Implicit Regularization in Variational Bayesian Matrix Factorization (SN, MS), pp. 815–822.
ICPRICPR-2010-Bouchaffra #network
Topological Dynamic Bayesian Networks (DB), pp. 898–901.
ICPRICPR-2010-CamposZJ
An Improved Structural EM to Learn Dynamic Bayesian Nets (CPdC, ZZ, QJ), pp. 601–604.
ICPRICPR-2010-KirbizCG #matrix #modelling
Bayesian Inference for Nonnegative Matrix Factor Deconvolution Models (SK, ATC, BG), pp. 2812–2815.
ICPRICPR-2010-PhilippotBB #algorithm #classification #learning #network #online
Bayesian Networks Learning Algorithms for Online Form Classification (EP, YB, AB), pp. 1981–1984.
ICPRICPR-2010-PiroNNB #classification
Boosting Bayesian MAP Classification (PP, RN, FN, MB), pp. 661–665.
ICPRICPR-2010-PuS #learning #probability #verification
Probabilistic Measure for Signature Verification Based on Bayesian Learning (DP, SNS), pp. 1188–1191.
ICPRICPR-2010-Tagawa #eye tracking #modelling #statistics #using
Depth Perception Model Based on Fixational Eye Movements Using Bayesian Statistical Inference (NT), pp. 1662–1665.
ICPRICPR-2010-TakasuFA #algorithm #similarity
A Variational Bayesian EM Algorithm for Tree Similarity (AT, DF, TA), pp. 1056–1059.
ICPRICPR-2010-TanveerI #approach #using
A Bayesian Approach to Face Hallucination Using DLPP and KRR (MT, NI), pp. 2154–2157.
ICPRICPR-2010-UyarBCB #development #network #predict
Bayesian Networks for Predicting IVF Blastocyst Development (AU, ABB, HNC, MB), pp. 2772–2775.
ICPRICPR-2010-Wang #detection #documentation #recognition #using
Document Logo Detection and Recognition Using Bayesian Model (HW), pp. 1961–1964.
ICPRICPR-2010-WirkertDC
Bayesian GOETHE Tracking (SW, ED, LC), pp. 2077–2080.
ICPRICPR-2010-ZhaoHDC #3d #automation #feature model #recognition #statistics
Automatic 3D Facial Expression Recognition Based on a Bayesian Belief Net and a Statistical Facial Feature Model (XZ, DH, ED, LC), pp. 3724–3727.
SEKESEKE-2010-BackerM #algorithm #network
Evaluating the Weighted Sum Algorithm for Estimating Conditional Probabilities in Bayesian Networks (SB, EM), pp. 319–324.
SEKESEKE-2010-GoelXS #multi #network #online #verification
A Multi-State Bayesian Network for Shill Verification in Online Auctions (AG, HX, SMS), pp. 279–285.
SEKESEKE-2010-Radlinski #development #predict #quality #using
Software Development Effort and Quality Prediction Using Bayesian Nets and small Local Qualitative Data (LR), pp. 113–116.
SACSAC-2010-AyyappanWN #algorithm #constraints #learning #named #network #scalability
MICHO: a scalable constraint-based algorithm for learning Bayesian networks (MA, YKW, WKN), pp. 985–989.
SACSAC-2010-LeeKL #network #personalisation #taxonomy
Applying taxonomic knowledge to Bayesian belief network for personalized search (JwL, HjK, SgL), pp. 1796–1801.
SACSAC-2010-QinXL #classification #nondeterminism
A Bayesian classifier for uncertain data (BQ, YX, FL), pp. 1010–1014.
ICSEICSE-2010-SridharanN #data analysis #re-engineering
Bayesian methods for data analysis in software engineering (MS, ASN), pp. 477–478.
CASECASE-2009-FanL #effectiveness #ranking
A Bayesian Ranking Scheme for supporting cost-effective yield diagnosis services (CMF, YPL), pp. 427–432.
DRRDRR-2009-Likforman-SulemS #classification #network #recognition
Combination of dynamic Bayesian network classifiers for the recognition of degraded characters (LLS, MS), pp. 1–10.
ICDARICDAR-2009-BarratT #image #modelling #network #using
Modeling, Classifying and Annotating Weakly Annotated Images Using Bayesian Network (SB, ST), pp. 1201–1205.
ICDARICDAR-2009-HamamuraAI09a #pattern matching #pattern recognition #recognition
Bayesian Best-First Search for Pattern Recognition — Application to Address Recognition (TH, TA, BI), pp. 461–465.
ICDARICDAR-2009-LuqmanBR #classification #graph #network #recognition #using
Graphic Symbol Recognition Using Graph Based Signature and Bayesian Network Classifier (MML, TB, JYR), pp. 1325–1329.
ICDARICDAR-2009-StefanoFFM #classification #evolution #learning #network
Learning Bayesian Networks by Evolution for Classifier Combination (CDS, FF, ASdF, AM), pp. 966–970.
ICDARICDAR-2009-Takasu #approximate #estimation #similarity
Bayesian Similarity Model Estimation for Approximate Recognized Text Search (AT), pp. 611–615.
VLDBVLDB-2009-MozafariZ #classification #naive bayes #privacy
Publishing Naive Bayesian Classifiers: Privacy without Accuracy Loss (BM, CZ), pp. 1173–1185.
ITiCSEITiCSE-2009-KasurinenN #programming
Estimating programming knowledge with Bayesian knowledge tracing (JK, UN), pp. 313–317.
CSMRCSMR-2009-FrankeJLUHEK #assessment #metric #network #using
A Method for Choosing Software Assessment Measures Using Bayesian Networks and Diagnosis (UF, PJ, RL, JU, DH, ME, JK), pp. 241–246.
HCIDHM-2009-MobusE #approach #modelling #programming #towards
Further Steps towards Driver Modeling According to the Bayesian Programming Approach (CM, ME), pp. 413–422.
CIKMCIKM-2009-KuoCW #learning #rank
Learning to rank from Bayesian decision inference (JWK, PJC, HMW), pp. 827–836.
CIKMCIKM-2009-MasadaFTHSO #analysis #optimisation #topic
Dynamic hyperparameter optimization for bayesian topical trend analysis (TM, DF, AT, TH, YS, KO), pp. 1831–1834.
ECIRECIR-2009-StathopoulosJ #automation #image
Bayesian Mixture Hierarchies for Automatic Image Annotation (VS, JMJ), pp. 138–149.
ICMLICML-2009-AdamsG #learning #named #parametricity
Archipelago: nonparametric Bayesian semi-supervised learning (RPA, ZG), pp. 1–8.
ICMLICML-2009-AdamsMM #parametricity #process
Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities (RPA, IM, DJCM), pp. 9–16.
ICMLICML-2009-CamposZJ #constraints #learning #network #using
Structure learning of Bayesian networks using constraints (CPdC, ZZ, QJ), pp. 113–120.
ICMLICML-2009-GarnettOR #predict
Sequential Bayesian prediction in the presence of changepoints (RG, MAO, SJR), pp. 345–352.
ICMLICML-2009-GermainLLM #classification #learning #linear
PAC-Bayesian learning of linear classifiers (PG, AL, FL, MM), pp. 353–360.
ICMLICML-2009-GuiverS #modelling #ranking
Bayesian inference for Plackett-Luce ranking models (JG, ES), pp. 377–384.
ICMLICML-2009-HaiderS #clustering #detection #email
Bayesian clustering for email campaign detection (PH, TS), pp. 385–392.
ICMLICML-2009-KamisettyL #approach #assessment #quality
A Bayesian approach to protein model quality assessment (HK, CJL), pp. 481–488.
ICMLICML-2009-KolterN #polynomial
Near-Bayesian exploration in polynomial time (JZK, AYN), pp. 513–520.
ICMLICML-2009-NickischS #linear #modelling #scalability
Convex variational Bayesian inference for large scale generalized linear models (HN, MWS), pp. 761–768.
ICMLICML-2009-RamanFWDR
The Bayesian group-Lasso for analyzing contingency tables (SR, TJF, PJW, ED, VR), pp. 881–888.
KDDKDD-2009-LiuGF #named
BBM: bayesian browsing model from petabyte-scale data (CL, FG, CF), pp. 537–546.
MLDMMLDM-2009-GaagRFGEBF #classification #network
Aligning Bayesian Network Classifiers with Medical Contexts (LCvdG, SR, AF, AdG, MJCE, FJB, BCJMF), pp. 787–801.
SIGIRSIGIR-2009-HuangH #approach #information retrieval #learning #ranking
A bayesian learning approach to promoting diversity in ranking for biomedical information retrieval (XH, QH), pp. 307–314.
SACSAC-2009-Ries #modelling #representation #trust
Extending Bayesian trust models regarding context-dependence and user friendly representation (SR), pp. 1294–1301.
SACSAC-2009-SherminO #network #using
Using dynamic bayesian networks to infer gene regulatory networks from expression profiles (AS, MAO), pp. 799–803.
SACSAC-2009-Villamarin-SalomonB #detection #similarity
Bayesian bot detection based on DNS traffic similarity (RVS, JCB), pp. 2035–2041.
WCREWCRE-2008-ZhouWGGL #approach #network #predict
A Bayesian Network Based Approach for Change Coupling Prediction (YZ, MW, EG, HG, JL), pp. 27–36.
ICALPICALP-A-2008-ChristodoulouKS #combinator
Bayesian Combinatorial Auctions (GC, AK, MS), pp. 820–832.
ICEISICEIS-AIDSS-2008-BringasPPS
Bayesian-Networks-Based Misuse and Anomaly Prevention System (PGB, YKP, SP, PS), pp. 62–69.
ICMLICML-2008-CaronD #parametricity
Sparse Bayesian nonparametric regression (FC, AD), pp. 88–95.
ICMLICML-2008-RaykarKBDR #automation #feature model #induction #learning #multi
Bayesian multiple instance learning: automatic feature selection and inductive transfer (VCR, BK, JB, MD, RBR), pp. 808–815.
ICMLICML-2008-ReisingerSM #kernel #learning #online
Online kernel selection for Bayesian reinforcement learning (JR, PS, RM), pp. 816–823.
ICMLICML-2008-SalakhutdinovM08a #markov #matrix #monte carlo #probability #using
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo (RS, AM), pp. 880–887.
ICMLICML-2008-SeegerN #design
Compressed sensing and Bayesian experimental design (MWS, HN), pp. 912–919.
ICMLICML-2008-SuZLM #learning #network #parametricity
Discriminative parameter learning for Bayesian networks (JS, HZ, CXL, SM), pp. 1016–1023.
ICPRICPR-2008-BarratT #effectiveness #image #network #retrieval #semantics #using #visual notation
Visual features with semantic combination using Bayesian network for a more effective image retrieval (SB, ST), pp. 1–4.
ICPRICPR-2008-Boccignone #analysis #parametricity #video
Nonparametric Bayesian attentive video analysis (GB), pp. 1–4.
ICPRICPR-2008-CamposJ #constraints #learning #network #parametricity #using
Improving Bayesian Network parameter learning using constraints (CPdC, QJ), pp. 1–4.
ICPRICPR-2008-ChenTZ #classification #novel
Spam filtering with several novel bayesian classifiers (CC, YT, CZ), pp. 1–4.
ICPRICPR-2008-Dahyot #classification #statistics
Bayesian classification for the Statistical Hough transform (RD), pp. 1–4.
ICPRICPR-2008-HeAP
A Bayesian Local Binary Pattern texture descriptor (CH, TA, MP), pp. 1–4.
ICPRICPR-2008-LiaoJ #learning #network #parametricity #semistructured data
Exploiting qualitative domain knowledge for learning Bayesian network parameters with incomplete data (WL, QJ), pp. 1–4.
ICPRICPR-2008-MatsuiCM #automation #detection
Bayesian sequential face detection with automatic re-initialization (AM, SC, TM), pp. 1–4.
ICPRICPR-2008-SukSL #gesture #modelling #network #recognition #robust
Robust modeling and recognition of hand gestures with dynamic Bayesian network (HIS, BKS, SWL), pp. 1–4.
ICPRICPR-2008-TagawaKNO #3d #image #multi #network #sequence
Direct 3-D shape recovery from image sequence based on multi-scale Bayesian network (NT, JK, SN, KO), pp. 1–4.
KDDKDD-2008-SatoYN #graph #information management #parametricity #semantics #using #word
Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model (IS, MY, HN), pp. 587–595.
KDDKDD-2008-SongJRG #linear
A bayesian mixture model with linear regression mixing proportions (XS, CJ, SR, JG), pp. 659–667.
SEKESEKE-2008-CemerlicYK #detection #network
Network Intrusion Detection Based on Bayesian Networks (AC, LY, JMK), pp. 791–794.
SIGIRSIGIR-2008-XuA #feedback
A bayesian logistic regression model for active relevance feedback (ZX, RA), pp. 227–234.
ICSTICST-2008-MirarabT #approach #empirical #testing
An Empirical Study on Bayesian Network-based Approach for Test Case Prioritization (SM, LT), pp. 278–287.
QoSAQoSA-2007-RoshandelMG #architecture #predict #reliability
A Bayesian Model for Predicting Reliability of Software Systems at the Architectural Level (RR, NM, LG), pp. 108–126.
CASECASE-2007-MoralesGM #effectiveness #fault #network #petri net
Diagnosis and Treatment of Faults in Productive Systems based on Bayesian Networks and Petri Net (RAGM, JIG, PEM), pp. 357–362.
DRRDRR-2007-Likforman-SulemS #network #recognition #using
Recognition of degraded handwritten digits using dynamic Bayesian networks (LLS, MS).
ICDARICDAR-2007-Likforman-SulemS #network #recognition #using
Recognition of Broken Characters from Historical Printed Books Using Dynamic Bayesian Networks (LLS, MS), pp. 173–177.
ICDARICDAR-2007-WillemsV #approach #detection #interactive #network
A Bayesian Network Approach to Mode Detection for Interactive Maps (DW, LV), pp. 869–873.
VLDBVLDB-2007-SilbersteinGMPY #approach
Making Sense of Suppressions and Failures in Sensor Data: A Bayesian Approach (AS, AEG, KM, GP, JY), pp. 842–853.
VLDBVLDB-2007-WuJ #set
A Bayesian Method for Guessing the Extreme Values in a Data Set (MW, CJ), pp. 471–482.
FASEFASE-2007-MirarabT #approach #network #testing
A Prioritization Approach for Software Test Cases Based on Bayesian Networks (SM, LT), pp. 276–290.
ICPCICPC-2007-MirarabHT #co-evolution #network #predict #using
Using Bayesian Belief Networks to Predict Change Propagation in Software Systems (SM, AH, LT), pp. 177–188.
HCIHIMI-MTT-2007-AblassmeierPRR #network #using
Context-Aware Information Agents for the Automotive Domain Using Bayesian Networks (MA, TP, SR, GR), pp. 561–570.
HCIHIMI-MTT-2007-LinL #analysis #automation
A Bayesian Methodology for Semi-automated Task Analysis (SCL, MRL), pp. 697–704.
ICEISICEIS-AIDSS-2007-StateCPS #approach #classification
A Connectionist Approach in Bayesian Classification (LS, CC, PV, VS), pp. 185–190.
CIKMCIKM-2007-LiuTZ #learning #network
Ensembling Bayesian network structure learning on limited data (FL, FT, QZ), pp. 927–930.
ECIRECIR-2007-YeungBCK #approach #documentation #learning
A Bayesian Approach for Learning Document Type Relevance (PCKY, SB, CLAC, MK), pp. 753–756.
ICMLICML-2007-GhavamzadehE #algorithm
Bayesian actor-critic algorithms (MG, YE), pp. 297–304.
ICMLICML-2007-Jaeger #learning #network #parametricity #relational
Parameter learning for relational Bayesian networks (MJ), pp. 369–376.
ICMLICML-2007-JiC #optimisation
Bayesian compressive sensing and projection optimization (SJ, LC), pp. 377–384.
ICMLICML-2007-KropotovV #learning #on the
On one method of non-diagonal regularization in sparse Bayesian learning (DK, DV), pp. 457–464.
ICMLICML-2007-TitovH #incremental #network #predict
Incremental Bayesian networks for structure prediction (IT, JH), pp. 887–894.
ICMLICML-2007-WilsonFRT #approach #learning #multi
Multi-task reinforcement learning: a hierarchical Bayesian approach (AW, AF, SR, PT), pp. 1015–1022.
ICMLICML-2007-YamazakiKWSM #fault
Asymptotic Bayesian generalization error when training and test distributions are different (KY, MK, SW, MS, KRM), pp. 1079–1086.
MLDMMLDM-2007-Holness #network
A Direct Measure for the Efficacy of Bayesian Network Structures Learned from Data (GH), pp. 601–615.
RecSysRecSys-2007-PronkVPT #classification #naive bayes #recommendation
Incorporating user control into recommender systems based on naive bayesian classification (VP, WFJV, AP, MT), pp. 73–80.
SIGIRSIGIR-2007-ZhangK #modelling #performance #recommendation
Efficient bayesian hierarchical user modeling for recommendation system (YZ, JK), pp. 47–54.
SACSAC-2007-ZhangMMB #locality #network #performance #problem #self #using
Performance problem localization in self-healing, service-oriented systems using Bayesian networks (RZ, SM, SM, AB), pp. 104–109.
CIKMCIKM-2006-ZigorisZ #adaptation #feedback #profiling
Bayesian adaptive user profiling with explicit & implicit feedback (PZ, YZ), pp. 397–404.
ECIRECIR-2006-Amati #approach #information retrieval
Frequentist and Bayesian Approach to Information Retrieval (GA), pp. 13–24.
ICMLICML-2006-Banerjee #bound #on the
On Bayesian bounds (AB), pp. 81–88.
ICMLICML-2006-GeJ #approximate #consistency #multi
A note on mixtures of experts for multiclass responses: approximation rate and Consistent Bayesian Inference (YG, WJ), pp. 329–335.
ICMLICML-2006-LeeGW #classification #using
Using query-specific variance estimates to combine Bayesian classifiers (CHL, RG, SW), pp. 529–536.
ICMLICML-2006-PoupartVHR #learning
An analytic solution to discrete Bayesian reinforcement learning (PP, NAV, JH, KR), pp. 697–704.
ICMLICML-2006-SilvaS #learning #metric #modelling
Bayesian learning of measurement and structural models (RBdAeS, RS), pp. 825–832.
ICMLICML-2006-SternHG #game studies #predict #ranking
Bayesian pattern ranking for move prediction in the game of Go (DHS, RH, TG), pp. 873–880.
ICMLICML-2006-SuZ #classification #network
Full Bayesian network classifiers (JS, HZ), pp. 897–904.
ICMLICML-2006-TingDS
Bayesian regression with input noise for high dimensional data (JAT, AD, SS), pp. 937–944.
ICMLICML-2006-XingSJT #multi #process #type inference
Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture (EPX, KAS, MIJ, YWT), pp. 1049–1056.
ICPRICPR-v1-2006-CarterYF #approach #behaviour #markov #recognition
A Combined Bayesian Markovian Approach for Behaviour Recognition (NLC, DPY, JMF), pp. 761–764.
ICPRICPR-v1-2006-DuCXL #interactive #network #process #using
Recognizing Interaction Activities using Dynamic Bayesian Network (YD, FC, WX, YL), pp. 618–621.
ICPRICPR-v1-2006-GormanTBH #behaviour #game studies #interactive
Bayesian Imitation of Human Behavior in Interactive Computer Games (BG, CT, CB, MH), pp. 1244–1247.
ICPRICPR-v1-2006-RiusVGV #performance
Action Spaces for Efficient Bayesian Tracking of Human Motion (IR, XV, JG, JJV), pp. 472–475.
ICPRICPR-v1-2006-WongC #classification #gesture #recognition #using
Continuous Gesture Recognition using a Sparse Bayesian Classifier (SFW, RC), pp. 1084–1087.
ICPRICPR-v1-2006-ZhouH #network #visual notation
Weighted Bayesian Network for Visual Tracking (YZ, TSH), pp. 523–526.
ICPRICPR-v2-2006-ChangYP #approach
An Iterative Bayesian Approach for Digital Matting (HC, QY, CP), pp. 122–125.
ICPRICPR-v2-2006-LernerM #classification #image #learning #network
Learning Bayesian Networks for Cytogenetic Image Classification (BL, RM), pp. 772–775.
ICPRICPR-v2-2006-McDanielKP #approach #classification #visual notation
A Bayesian Approach to Visual Size Classification of Everyday Objects (TLM, KK, SP), pp. 255–259.
ICPRICPR-v3-2006-BeveridgeSR #classification #comparison #detection #naive bayes #using
A Comparison of Pixel, Edge andWavelet Features for Face Detection using a Semi-Naive Bayesian Classifier (JRB, JS, BR), pp. 1175–1178.
ICPRICPR-v3-2006-HarmoucheCAFA #classification #modelling
Bayesian MS Lesion Classification Modeling Regional and Local Spatial Information (RH, DLC, DLA, SJF, TA), pp. 984–987.
ICPRICPR-v3-2006-JainML #clustering #feedback
Bayesian Feedback in Data Clustering (AKJ, PKM, MHCL), pp. 374–378.
ICPRICPR-v3-2006-MaKKLK #estimation
Sparse Bayesian Regression for Head Pose Estimation (YM, YK, KK, SL, MK), pp. 507–510.
ICPRICPR-v3-2006-MatosC #classification #network
Combining global and local classifiers with Bayesian network (LNM, JMdC), pp. 1212–1215.
ICPRICPR-v3-2006-PalenichkaZ #image #network #using
Perceptual Knowledge Extraction Using Bayesian Networks of Salient Image Objects (RMP, MBZ), pp. 1216–1219.
ICPRICPR-v3-2006-WongWC #classification #robust #using
Robust Appearance-based Tracking using a sparse Bayesian classifier (SFW, KYKW, RC), pp. 47–50.
ICPRICPR-v4-2006-LinO #network #recognition #speech
Switching Auxiliary Chains for Speech Recognition based on Dynamic Bayesian Networks (HL, ZO), pp. 258–261.
ICPRICPR-v4-2006-LiuH #automation #predict #segmentation #speech
A Bayesian Predictive Method for Automatic Speech Segmentation (ML, TSH), pp. 290–293.
ICPRICPR-v4-2006-MatosC06a #classification #network
Combining global and local classifiers with Bayesian network (LNM, JMdC), p. 952.
ICPRICPR-v4-2006-PalenichkaZ06a #image #network #using
Perceptual Knowledge Extraction Using Bayesian Networks of Salient Image Objects (RMP, MBZ), p. 953.
ICPRICPR-v4-2006-WangK #approach #automation #estimation #using
Automatic Alignment of High-Resolution NMR Spectra Using a Bayesian Estimation Approach (ZW, SBK), pp. 667–670.
SEKESEKE-2006-HaiderC #estimation #fault
Bayesian Estimation of Defects based on Defect Decay Model: BayesED3M (SWH, JWC), pp. 256–261.
SACSAC-2006-MerschmannP #approach #classification
A Bayesian approach for protein classification (LHdCM, AP), pp. 200–201.
ICSEICSE-2006-AbeMKKH #classification #estimation #using
Estimation of project success using Bayesian classifier (SA, OM, TK, NK, MH), pp. 600–603.
ICSEICSE-2006-MandelinKY #approach #architecture #diagrams #modelling
A Bayesian approach to diagram matching with application to architectural models (DM, DK, DMY), pp. 222–231.
WICSAWICSA-2005-TangJHN #architecture #design #impact analysis #network #predict
Predicting Change Impact in Architecture Design with Bayesian Belief Networks (AT, YJ, JH, AEN), pp. 67–76.
ICDARICDAR-2005-HtweHLY #network #online
Transliteration of Online Handwritten Phonetic Pitman’s Shorthand with the Use of a Bayesian Network (SMH, CH, GL, MY), pp. 1090–1094.
SASSAS-2005-JungKSY #analysis #c #statistics
Taming False Alarms from a Domain-Unaware C Analyzer by a Bayesian Statistical Post Analysis (YJ, JK, JS, KY), pp. 203–217.
ICEISICEIS-v2-2005-ColaceSVF #algorithm #approach #learning #multi #network
A Bayesian Networks Structural Learning Algorithm Based on a Multiexpert Approach (FC, MDS, MV, PF), pp. 194–200.
ICMLICML-2005-AngelopoulosC
Tempering for Bayesian C&RT (NA, JC), pp. 17–24.
ICMLICML-2005-BurgeL #learning #network
Learning class-discriminative dynamic Bayesian networks (JB, TL), pp. 97–104.
ICMLICML-2005-ChengJSW #image #modelling
Variational Bayesian image modelling (LC, FJ, DS, SW), pp. 129–136.
ICMLICML-2005-GirolamiR #kernel #learning #modelling
Hierarchic Bayesian models for kernel learning (MG, SR), pp. 241–248.
ICMLICML-2005-HellerG #clustering
Bayesian hierarchical clustering (KAH, ZG), pp. 297–304.
ICMLICML-2005-JingPR #classification #learning #naive bayes #network #performance
Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes (YJ, VP, JMR), pp. 369–376.
ICMLICML-2005-KoivistoS #aspect-oriented #modelling
Computational aspects of Bayesian partition models (MK, KS), pp. 433–440.
ICMLICML-2005-PernkopfB #classification #generative #learning #network #parametricity
Discriminative versus generative parameter and structure learning of Bayesian network classifiers (FP, JAB), pp. 657–664.
ICMLICML-2005-SnelsonG #approximate #predict
Compact approximations to Bayesian predictive distributions (ES, ZG), pp. 840–847.
ICMLICML-2005-WangLBS #online #optimisation
Bayesian sparse sampling for on-line reward optimization (TW, DJL, MHB, DS), pp. 956–963.
KDDKDD-2005-ChenH #analysis #classification #image #network
A Bayesian network classifier with inverse tree structure for voxelwise magnetic resonance image analysis (RC, EH), pp. 4–12.
KDDKDD-2005-JaroszewiczS #network #performance
Fast discovery of unexpected patterns in data, relative to a Bayesian network (SJ, TS), pp. 118–127.
MLDMMLDM-2005-KarrasMGO #mining
Improved MRI Mining by Integrating Support Vector Machine Priors in the Bayesian Restoration (DAK, BGM, DGD, DvO), pp. 325–333.
SEKESEKE-2005-LienT #network #recommendation #web
A Web Pages Recommender with Bayesian Networks (CCL, HLT), pp. 82–87.
ICEISICEIS-v2-2004-BendouM #graph #learning #network
Learning Bayesian Networks with Largest Chain Graphs (MB, PM), pp. 184–190.
ICEISICEIS-v2-2004-ColaceSVF #algorithm #automation #learning #ontology
A Semi-Automatic Bayesian Algorithm for Ontology Learning (FC, MDS, MV, PF), pp. 191–196.
ICEISICEIS-v2-2004-ColaceSVF04a #algorithm #comparison #learning #network
Bayesian Network Structural Learning from Data: An Algorithms Comparison (FC, MDS, MV, PF), pp. 527–530.
ICMLICML-2004-DSouzaVS
The Bayesian backfitting relevance vector machine (AD, SV, SS).
ICMLICML-2004-GrossmanD #classification #learning #network
Learning Bayesian network classifiers by maximizing conditional likelihood (DG, PMD).
ICMLICML-2004-XingSJ #process #type inference
Bayesian haplo-type inference via the dirichlet process (EPX, RS, MIJ).
ICMLICML-2004-ZhangYK #algorithm #kernel #learning #matrix #using
Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm (ZZ, DYY, JTK).
ICPRICPR-v1-2004-KangD #approximate #bound #classification #fault
Product Approximation by Minimizing the Upper Bound of Bayes Error Rate for Bayesian Combination of Classifiers (HJK, DSD), pp. 252–255.
ICPRICPR-v1-2004-LombardiZ #architecture #design
Architectural Design Issues for Bayesian Contextual Vision (PL, BZ), pp. 753–756.
ICPRICPR-v1-2004-PereraH #detection
Bayesian Object-Level Change Detection in Grayscale Imagery (AGAP, AH), pp. 71–75.
ICPRICPR-v1-2004-SunG #image #markov #random #segmentation
Bayesian Image Segmentation Based on an Inhomogeneous Hidden Markov Random Field (JS, DG), pp. 596–599.
ICPRICPR-v2-2004-EngWKY #detection #framework #robust #using
A Bayesian Framework for Robust Human Detection and Occlusion Handling using Human Shape Model (HLE, JW, AHK, WYY), pp. 257–260.
ICPRICPR-v2-2004-JorgeMA #architecture #estimation #network #sequence #video
Estimation of the Bayesian Network Architecture for Object Tracking in Video Sequences (PMJ, JSM, AJA), pp. 732–735.
ICPRICPR-v2-2004-KagehiroKSF
Address-Block Extraction by Bayesian Rule (TK, MK, HS, HF), pp. 582–585.
ICPRICPR-v2-2004-KaneS #classification #image #learning #network
Bayesian Network Structure Learning and Inference in Indoor vs. Outdoor Image Classification (MJK, AES), pp. 479–482.
ICPRICPR-v2-2004-Nagao #approach #feature model #kernel
Bayesian Approach with Nonlinear Kernels to Feature Extraction (KN), pp. 153–156.
ICPRICPR-v2-2004-SebeCHG #approach #detection #network
Skin Detection: A Bayesian Network Approach (NS, IC, TSH, TG), pp. 903–906.
ICPRICPR-v2-2004-WangDZSLB #approach #multi #network #visual notation
A Dynamic Bayesian Network Approach to Multi-cue based Visual Tracking (TW, QD, YZ, GS, CL, GRB), pp. 167–170.
ICPRICPR-v2-2004-ZhangZ #automation #concept #framework #image
A Bayesian Framework for Automatic Concept Discovery in Image Collections (RZ, Z(Z), pp. 973–976.
ICPRICPR-v3-2004-AnsaryVD #3d #approach #modelling #retrieval
A Bayesian Approach for 3D Models Retrieval Based on Characteristic Views (TFA, JPV, MD), pp. 898–901.
ICPRICPR-v3-2004-GurwiczL #agile #estimation #kernel #network
Rapid Spline-based Kernel Density Estimation for Bayesian Networks (YG, BL), pp. 700–703.
ICPRICPR-v3-2004-LifshitsBGRR #geometry
Rehashing for Bayesian Geometric Hashing (ML, IB, RG, ER, MR), pp. 99–102.
ICPRICPR-v3-2004-MatsuiCUM #markov #monte carlo #recognition #using
Bayesian Face Recognition using a Markov Chain Monte Carlo Method (AM, SC, FU, TM), pp. 918–921.
ICPRICPR-v4-2004-BoubchirFB #approximate #using
Bayesian Denoising in the Wavelet-Domain Using an Analytical Approximate a-Stable Prior (LB, MJF, DB), pp. 889–892.
ICPRICPR-v4-2004-ChantasGL #image
Non Stationary Bayesian Image Restoration (GKC, NPG, AL), pp. 689–692.
ICPRICPR-v4-2004-WangT #modelling #recognition
Bayesian Face Recognition Based on Gaussian Mixture Models (XW, XT), pp. 142–145.
ICPRICPR-v4-2004-ZhouWG #estimation #using
Tracking Periodic Motion using Bayesian Estimation (HZ, AMW, PRG), pp. 725–728.
KDDKDD-2004-JaroszewiczS #network #using
Interestingness of frequent itemsets using Bayesian networks as background knowledge (SJ, DAS), pp. 178–186.
KDDKDD-2004-Poole #approach
Estimating the size of the telephone universe: a Bayesian Mark-recapture approach (DP), pp. 659–664.
KDDKDD-2004-SmithE #framework #network
A Bayesian network framework for reject inference (ATS, CE), pp. 286–295.
KDDKDD-2004-WrightY #distributed #network #privacy #semistructured data
Privacy-preserving Bayesian network structure computation on distributed heterogeneous data (RNW, ZY), pp. 713–718.
SIGIRSIGIR-2004-YuTY #framework #information management #parametricity
A nonparametric hierarchical bayesian framework for information filtering (KY, VT, SY), pp. 353–360.
SIGIRSIGIR-2004-Zhang #adaptation #classification #using
Using bayesian priors to combine classifiers for adaptive filtering (YZ0), pp. 345–352.
DACDAC-2003-FineZ #functional #generative #network #testing #using #verification
Coverage directed test generation for functional verification using bayesian networks (SF, AZ), pp. 286–291.
ICDARICDAR-2003-ChoiCK #generative #network #online
Generation of Handwritten Characters with Bayesian network based On-line Handwriting Recognizers (HIC, SJC, JHK), p. 995–?.
ICDARICDAR-2003-ChoK #modelling #network #online #recognition
Bayesian Network Modeling of Hangul Characters for On-line Handwriting Recognition (SJC, JHK), pp. 207–211.
ICEISICEIS-v2-2003-BendouM #learning #network #semistructured data
Learning Bayesian Networks From Noisy Data (MB, PM), pp. 26–33.
ICEISICEIS-v2-2003-CamposGM #abduction #network #probability #using
Partial Abductive Inference in Bayesian Networks By Using Probability Trees (LMdC, JAG, SM), pp. 83–91.
ICEISICEIS-v2-2003-ColaceSFV #learning #network #ontology
Ontology Learning Through Bayesian Networks (FC, MDS, PF, MV), pp. 430–433.
ICEISICEIS-v2-2003-Koehler #automation #database #health #learning #network
Tool for Automatic Learning of Bayesian Networks From Database: An Application in the Health Area (CK), pp. 474–481.
ICMLICML-2003-CerquidesM #learning #modelling #naive bayes
Tractable Bayesian Learning of Tree Augmented Naive Bayes Models (JC, RLdM), pp. 75–82.
ICMLICML-2003-Duff03a #approximate #markov
Diffusion Approximation for Bayesian Markov Chains (MOD), pp. 139–146.
ICMLICML-2003-MooreW #learning #network
Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning (AWM, WKW), pp. 552–559.
ICMLICML-2003-WongMCW #detection #network
Bayesian Network Anomaly Pattern Detection for Disease Outbreaks (WKW, AWM, GFC, MMW), pp. 808–815.
ICMLICML-2003-ZhangXC #adaptation #learning
Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning (YZ, WX, JPC), pp. 896–903.
KDDKDD-2003-FramAD #data mining #empirical #mining #safety
Empirical Bayesian data mining for discovering patterns in post-marketing drug safety (DMF, JSA, WD), pp. 359–368.
MLDMMLDM-2003-DeventerDNK #modelling #testing #using
Using Test Plans for Bayesian Modeling (RD, JD, HN, OK), pp. 307–316.
MLDMMLDM-2003-KimJ #network #using
Using Bayesian Networks to Analyze Medical Data (ICK, YGJ), pp. 317–327.
SEKESEKE-2003-Morasca #approach #evaluation #testing
A Bayesian Approach to Software Testing Evaluation (SM), pp. 706–713.
SIGIRSIGIR-2003-ZaragozaHT #ad hoc #information retrieval
Bayesian extension to the language model for ad hoc information retrieval (HZ, DH, MET), pp. 4–9.
SACSAC-2003-Leon-RojasMM #fuzzy #on the
On the Fuzzy Bayesian Inference of Population Annoyance Level Caused by Noise Exposure (JMLR, VM, MM), pp. 227–234.
ECIRECIR-2002-CamposFH #documentation #network #retrieval
A Layered Bayesian Network Model for Document Retrieval (LMdC, JMFL, JFH), pp. 169–182.
ECIRECIR-2002-TsikrikaL #documentation #network #using #web
Combining Web Document Representations in a Bayesian Inference Network Model Using Link and Content-Based Evidence (TT, ML), pp. 53–72.
ICMLICML-2002-DashC #classification #naive bayes
Exact model averaging with naive Bayesian classifiers (DD, GFC), pp. 91–98.
ICMLICML-2002-ThamDR #classification #learning #markov #monte carlo #using
Sparse Bayesian Learning for Regression and Classification using Markov Chain Monte Carlo (SST, AD, KR), pp. 634–641.
ICMLICML-2002-ZhangL #bound #network
Representational Upper Bounds of Bayesian Networks (HZ, CXL), pp. 674–681.
ICPRICPR-v1-2002-HungTL #3d #approach #segmentation #video
A Bayesian Approach to Video Object Segmentation via Merging 3D Watershed Volumes (YPH, YPT, CCL), pp. 496–499.
ICPRICPR-v1-2002-LezorayC #image
Bayesian Marker Extraction for Color Watershed in Segmenting Microscopic Images (OL, HC), pp. 739–742.
ICPRICPR-v1-2002-Mignotte #multi #parametricity
Bayesian Rendering with Non-Parametric Multiscale Prior Model (MM), pp. 247–251.
ICPRICPR-v1-2002-Souafi-BensafiPLE #classification #documentation #network
Bayesian Networks Classifiers Applied to Documents (SSB, MP, FL, HE), p. 483–?.
ICPRICPR-v2-2002-GargPH #classification #network
Bayesian Networks as Ensemble of Classifiers (AG, VP, TSH), pp. 779–784.
ICPRICPR-v2-2002-SkaffAC
Active Bayesian Color Constancy with Non-Uniform Sensors (SS, TA, JJC), pp. 681–684.
ICPRICPR-v3-2002-BaesensECV #classification #learning #markov #monte carlo #network #using
Learning Bayesian Network Classifiers for Credit Scoring Using Markov Chain Monte Carlo Search (BB, MEP, RC, JV), pp. 49–52.
ICPRICPR-v3-2002-ChoudhuryRPP #detection #learning #network
Boosting and Structure Learning in Dynamic Bayesian Networks for Audio-Visual Speaker Detection (TC, JMR, VP, AP), p. 789–?.
ICPRICPR-v3-2002-CooperWABCHKKLOVVJKLM #geometry #problem
Bayesian Pot-Assembly from Fragments as Problems in Perceptual-Grouping and Geometric-Learning (DBC, ARW, SA, JB, YC, DH, KK, WK, FFL, XO, SV, EV, MSJ, BBK, DHL, DM), pp. 297–302.
ICPRICPR-v3-2002-QianCZ #approach #estimation #independence #multi
A Bayesian Approach to Simultaneous Motion Estimation of Multiple Independently Moving Objects (GQ, RC, QZ), p. 309–?.
ICPRICPR-v3-2002-QiP #classification
Context-Sensitive Bayesian Classifiers and Application to Mouse Pressure Pattern Classification (Y(Q, RWP), pp. 448–451.
ICPRICPR-v3-2002-SilvestreL #bound #classification #optimisation
Optimization of Neural Classifiers Based on Bayesian Decision Boundaries and Idle Neurons Pruning (MRS, LLL), pp. 387–390.
ICPRICPR-v4-2002-MathisB #approach #classification #using
Classification Using a Hierarchical Bayesian Approach (CM, TMB), p. 103–?.
ICPRICPR-v4-2002-StephensonMB #automation #network #recognition #speech
Mixed Bayesian Networks with Auxiliary Variables for Automatic Speech Recognition (TAS, MMD, HB), p. 293–?.
KDDKDD-2002-AntalGF #clustering #learning #network #on the
On the potential of domain literature for clustering and Bayesian network learning (PA, PG, GF), pp. 405–414.
KDDKDD-2002-RidgewayM #analysis #dataset
Bayesian analysis of massive datasets via particle filters (GR, DM), pp. 5–13.
KRKR-2002-BeygelzimerR #complexity #learning #network
Inference Complexity as a Model-Selection Criterion for Learning Bayesian Networks (AB, IR), pp. 558–567.
SIGIRSIGIR-2002-ChaiCN #classification #online
Bayesian online classifiers for text classification and filtering (KMAC, HLC, HTN), pp. 97–104.
SACSAC-2002-ElishRF #collaboration #learning #network
Evaluating collaborative software in supporting organizational learning with Bayesian Networks (MOE, DCR, JEF), pp. 992–996.
DACDAC-2001-BhanjaR #dependence #modelling #network #probability #process #using
Dependency Preserving Probabilistic Modeling of Switching Activity using Bayesian Networks (SB, NR), pp. 209–214.
ICDARICDAR-2001-ChoK #modelling #network #online #recognition
Bayesian Network Modeling of Strokes and their Relationships for On-line Handwriting Recognition (SJC, JHK), pp. 86–90.
ICDARICDAR-2001-HasegawaAM #algorithm #online #recognition
A Bayesian Bi-gram Scheme for HMM Online Handwriting Recognition Algorithm (TH, KA, TM), pp. 1012–1016.
ICSMEICSM-2001-Chulani #analysis #modelling #quality
Bayesian Analysis of Software Cost and Quality Models (SC), p. 565–?.
ICEISICEIS-v1-2001-GamboaF #approach #design
Designing Intelligent Tutoring Systems: A Bayesian Approach (HG, ALNF), pp. 452–458.
ICMLICML-2001-ChuKO #framework
A Unified Loss Function in Bayesian Framework for Support Vector Regression (WC, SSK, CJO), pp. 51–58.
ICMLICML-2001-GartnerF #classification #named
WBCsvm: Weighted Bayesian Classification based on Support Vector Machines (TG, PAF), pp. 154–161.
ICMLICML-2001-HamerlyE #predict
Bayesian approaches to failure prediction for disk drives (GH, CE), pp. 202–209.
ICMLICML-2001-NgJ #classification #convergence #feature model
Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection (AYN, MIJ), pp. 377–384.
ICMLICML-2001-ZadroznyE #classification #naive bayes #probability
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers (BZ, CE), pp. 609–616.
MLDMMLDM-2001-PhamWS #classification #detection #network
Face Detection by Aggregated Bayesian Network Classifiers (TVP, MW, AWMS), pp. 249–262.
SEKESEKE-2001-VincenziNMDR #guidelines
Bayesian-Learning Based Guidelines to determine Equivalente Mutants (AMRV, EYN, JCM, MED, RAFR), pp. 180–187.
ICSTSAT-2001-HorvitzRGKSC #approach #problem
A Bayesian Approach to Tackling Hard Computational Problems (Preliminary Report) (EH, YR, CPG, HAK, BS, DMC), pp. 376–391.
ICEISICEIS-2000-Emery #case study #network
The Use of Information Theory in the Construction of a Bayesian Belief Network: A Case Study (DEE), pp. 121–126.
ICEISICEIS-2000-MilhoF #development #network
A User-Friendly Development Tool for Medical Diagnosis Based on Bayesian Networks (IM, ALNF), pp. 176–180.
ICMLICML-2000-Domingos #classification #problem
Bayesian Averaging of Classifiers and the Overfitting Problem (PMD), pp. 223–230.
ICMLICML-2000-HsuHW #classification #naive bayes #why
Why Discretization Works for Naive Bayesian Classifiers (CNH, HJH, TTW), pp. 399–406.
ICMLICML-2000-KimN #learning #network #set
Learning Bayesian Networks for Diverse and Varying numbers of Evidence Sets (ZWK, RN), pp. 479–486.
ICMLICML-2000-LiB #approach #clustering #markov #modelling #using
A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models (CL, GB), pp. 543–550.
ICMLICML-2000-Strens #framework #learning
A Bayesian Framework for Reinforcement Learning (MJAS), pp. 943–950.
ICPRICPR-v1-2000-HongengBN #framework #video
Bayesian Framework for Video Surveillance Application (SH, FB, RN), pp. 1164–1170.
ICPRICPR-v1-2000-PalettaPP #analysis #learning #recognition #using
Learning Temporal Context in Active Object Recognition Using Bayesian Analysis (LP, MP, AP), pp. 1695–1699.
ICPRICPR-v1-2000-PiaterG #learning #network #recognition
Feature Learning for Recognition with Bayesian Networks (JHP, RAG), pp. 1017–1020.
ICPRICPR-v2-2000-LingC #bound #feature model #performance
Fast and Efficient Feature Extraction Based on Bayesian Decision Boundaries (LLL, HMC), pp. 2390–2393.
ICPRICPR-v2-2000-RitterG #approach #identification #pattern matching #pattern recognition #recognition
A Bayesian Approach to Object Identification in Pattern Recognition (GR, MTG), pp. 2418–2421.
ICPRICPR-v2-2000-VailayaJ #classification
Reject Option for VQ-Based Bayesian Classification (AV, AKJ), pp. 2048–2051.
ICPRICPR-v2-2000-ValvenyM #documentation #framework #recognition #using
Hand-Drawn Symbol Recognition in Graphic Documents Using Deformable Template Matching and a Bayesian Framework (EV, EM), pp. 2239–2242.
ICPRICPR-v3-2000-DemireklerKC #using
Fusing Length and Voicing Information, and HMM Decision Using a Bayesian Causal Tree against Insufficient Training Data (MD, FK, ), pp. 3106–3109.
ICPRICPR-v3-2000-LuJ #approach #image
A New Bayesian Approach to Image Denoising with a Combination of MRFs and Pixon Method (QL, TJ), pp. 3734–3737.
ICPRICPR-v3-2000-MostafaPF #classification
A Two-Step Fuzzy-Bayesian Classification for High Dimensional Data (MGHM, TCP, AAF), pp. 3421–3424.
KDDKDD-2000-KontkanenLMT #visualisation
Unsupervised Bayesian visualization of high-dimensional data (PK, JL, PM, HT), pp. 325–329.
KDDKDD-2000-Tresp
The generalized Bayesian committee machine (VT), pp. 130–139.
KRKR-2000-Yelland #logic #network
An Alternative Combination of Bayesian Networks and Description Logics (PMY), pp. 225–234.
SIGIRSIGIR-2000-AndroutsopoulosKCS #anti #comparison #email #keyword #naive bayes
An experimental comparison of naive bayesian and keyword-based anti-spam filtering with personal e-mail messages (IA, JK, KC, CDS), pp. 160–167.
ICLPCL-2000-Poole #information management #logic #representation
Logic, Knowledge Representation, and Bayesian Decision Theory (DP), pp. 70–86.
ICEISICEIS-1999-Habrant #database #learning #network #predict #search-based
Structure Learning of Bayesian Networks from Databases by Genetic Algorithms-Application to Time Series Prediction in Finance (JH), pp. 225–231.
ICMLICML-1999-LangleyS #analysis #classification #naive bayes
Tractable Average-Case Analysis of Naive Bayesian Classifiers (PL, SS), pp. 220–228.
ICMLICML-1999-ZhengWT #lazy evaluation #learning #naive bayes
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees (ZZ, GIW, KMT), pp. 493–502.
KDDKDD-1999-Cerquides #induction
Applying General Bayesian Techniques to Improve TAN Induction (JC), pp. 292–296.
KDDKDD-1999-DaviesM #dataset #network
Bayesian Networks for Lossless Dataset Compression (SD, AWM), pp. 387–391.
ICMLICML-1998-CristianiniSS #classification #scalability
Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space (NC, JST, PS), pp. 109–117.
ICMLICML-1998-FriedmanGL #classification #network #parametricity
Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting (NF, MG, TJL), pp. 179–187.
ICMLICML-1998-Heskes #approach #learning #multi
Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach (TH), pp. 233–241.
ICMLICML-1998-RamachandranM #network #refinement
Theory Refinement of Bayesian Networks with Hidden Variables (SR, RJM), pp. 454–462.
ICPRICPR-1998-BamfordL #analysis #segmentation
Bayesian analysis of cell nucleus segmentation by a Viterbi search based active contour (PB, BCL), pp. 133–135.
ICPRICPR-1998-CallariSF #approach #estimation #nondeterminism
Uncertainty in pose estimation: a Bayesian approach (FGC, GS, FPF), pp. 972–976.
ICPRICPR-1998-KrebsW #3d #automation #generative #recognition
Automatic generation of Bayesian nets for 3D object recognition (BK, FMW), pp. 126–128.
ICPRICPR-1998-LamOX #classification #learning
Application of Bayesian Ying-Yang criteria for selecting the number of hidden units with backpropagation learning to electrocardiogram classification (WKL, NO, LX), pp. 1686–1688.
KDDKDD-1998-KontkanenMST #classification #feature model #named
BAYDA: Software for Bayesian Classification and Feature Selection (PK, PM, TS, HT), pp. 254–258.
KRKR-1998-Jaeger #infinity #network #random #reasoning #relational
Reasoning About Infinite Random Structures with Relational Bayesian Networks (MJ), pp. 570–581.
LICSLICS-1998-Jaeger #convergence #network #relational
Convergence Results for Relational Bayesian Networks (MJ), pp. 44–55.
ICSMEICSM-1997-ZivR #maintenance #modelling #nondeterminism #testing
Constructing Bayesian-network models of software testing and maintenance uncertainties (HZ, DJR), p. 100–?.
ICMLICML-1997-SuematsuHL #approach #learning #markov
A Bayesian Approach to Model Learning in Non-Markovian Environments (NS, AH, SL), pp. 349–357.
KDDKDD-1997-Domingos #why
Why Does Bagging Work? A Bayesian Account and its Implications (PMD), pp. 155–158.
KDDKDD-1997-TurmonMP #identification
Bayesian Inference for Identifying Solar Active Regions (MJT, SM, JP), pp. 267–270.
KDDAKDDM-1996-CheesemanS #classification
Bayesian Classification (AutoClass): Theory and Results (PC, JS), pp. 153–180.
KDDAKDDM-1996-Heckerman #information management #network
Bayesian Networks for Knowledge Discovery (DH), pp. 273–305.
ICMLICML-1996-DomingosP #classification #independence
Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier (PMD, MJP), pp. 105–112.
ICMLICML-1996-EzawaSN #learning #network #risk management
Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management (KJE, MS, SWN), pp. 139–147.
ICMLICML-1996-FriedmanG #learning #network
Discretizing Continuous Attributes While Learning Bayesian Networks (NF, MG), pp. 157–165.
ICMLICML-1996-SinghP #classification #learning #network #performance
Efficient Learning of Selective Bayesian Network Classifiers (MS, GMP), pp. 453–461.
ICMLICML-1996-Suzuki #algorithm #learning #network #performance #using
Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: An Efficient Algorithm Using the B & B Technique (JS), pp. 462–470.
ICPRICPR-1996-Brailovsky #approach #detection #probability
An approach to outlier detection based on Bayesian probabilistic model (VLB), pp. 70–74.
ICPRICPR-1996-CoxMOY #feedback #image #named #retrieval
PicHunter: Bayesian relevance feedback for image retrieval (IJC, MLM, SMO, PNY), pp. 361–369.
ICPRICPR-1996-DiasBD #classification #identification
Results of the use of Bayesian classifiers for identification of breast cancer cell nuclei (ÂVD, FB, MRD), pp. 508–512.
ICPRICPR-1996-Gimelfarb96a #modelling #segmentation #simulation
Gibbs models for Bayesian simulation and segmentation of piecewise-uniform textures (GLG), pp. 760–764.
ICPRICPR-1996-MoghaddamNP #image #similarity
A Bayesian similarity measure for direct image matching (BM, CN, AP), pp. 350–358.
ICPRICPR-1996-PrantlGP #multi #network #using
Active fusion using Bayesian networks applied to multi-temporal remote sensing imagery (MP, HG, AP), pp. 890–894.
ICPRICPR-1996-UtschickN #adaptation #network
Bayesian adaptation of hidden layers in Boolean feedforward neural networks (WU, JAN), pp. 229–233.
ICPRICPR-1996-YimBA #segmentation #using
Bayesian range segmentation using focus cues (CY, ACB, JKA), pp. 482–486.
KDDKDD-1996-Sahami #classification #dependence #learning
Learning Limited Dependence Bayesian Classifiers (MS), pp. 335–338.
CIKMCIKM-1995-ParkHC #automation #network #using
Automatic Thesaurus Construction Using Bayesian Networks (YCP, YSH, KSC), pp. 212–217.
ICMLICML-1995-Cussens #algorithm #analysis #finite #learning
A Bayesian Analysis of Algorithms for Learning Finite Functions (JC), pp. 142–149.
ICMLICML-1995-Heckerman #learning #network
Learning With Bayesian Networks (Abstract) (DH), p. 588.
ICMLICML-1995-SinghP #algorithm #classification #comparison #induction
A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers (MS, GMP), pp. 497–505.
KDDKDD-1995-EzawaN #information management #modelling #network #using
Knowledge Discovery in Telecommunication Services Data Using Bayesian Network Models (KJE, SWN), pp. 100–105.
KDDKDD-1995-Pazzani #approach #classification
An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers (MJP), pp. 228–233.
KDDKDD-1995-SpirtesM #learning #network
Learning Bayesian Networks with Discrete Variables from Data (PS, CM), pp. 294–299.
KDDKDD-1995-Thiesson #network #quantifier #semistructured data
Accelerated Quantification of Bayesian Networks with Incomplete Data (BT), pp. 306–311.
SACSAC-1995-HalgamugeGG #fuzzy #network #problem #prototype
A sub Bayesian nearest prototype neural network with fuzzy interpretability for diagnosis problems (SKH, CG, MG), pp. 445–449.
ICMLICML-1994-Muggleton #induction #logic programming
Bayesian Inductive Logic Programming (SM), pp. 371–379.
KDDKDD-1994-HeckermanGC #learning #network #statistics
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data (DH, DG, DMC), pp. 85–96.
CIKMCIKM-1993-EickJ #algorithm #classification #learning #search-based
Learning Bayesian Classification Rules through Genetic Algorithms (CFE, DJ), pp. 305–313.
ICMLICML-1993-Connolly #clustering #concept #network
Constructing Hidden Variables in Bayesian Networks via Conceptual Clustering (DC), pp. 65–72.
ICMLICML-1993-Schwalb #compilation #network
Compiling Bayesian Networks into Neural Networks (ES), pp. 291–297.
SIGIRSIGIR-1993-TzerasH #automation #network
Automatic Indexing Based on Bayesian Inference Networks (KT, SH), pp. 22–34.
KDDKDD-1991-WuSO #classification #heuristic #integration
Integration of Heuristic and Bayesian Approaches in a Pattern-Classification System (QW, PS, AO), pp. 249–260.
CHICHI-1989-Cole #comprehension #reasoning #visual notation
Understanding Bayesian reasoning via graphical displays (WGC), pp. 381–386.
ICMLML-1988-CheesemanKSSTF #classification #named
AutoClass: A Bayesian Classification System (PC, JK, MS, JS, WT, DF), pp. 54–64.
SIGIRSIGIR-1984-KraftB #behaviour #information retrieval #roadmap
Advances in a Bayesian Decision Model of User Stopping Behaviour for Scanning the Output of an Information Retrieval System (DHK, DAB), pp. 421–433.
ICSEICSE-1976-Chandy #design #modelling
Bayesian Models of Design Based on Intuition (KMC), pp. 281–285.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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