Stem bayesian$ (all stems)

508 papers:

CASE-2015-SabbaghiHD #3d #modelling #quality

Bayesian additive modeling for quality control of 3D printed products (AS, QH, TD), pp. 906–911.

CASE-2015-SterlingSZC #algorithm #optimisation #parametricity #process

Welding parameter optimization based on Gaussian process regression Bayesian optimization algorithm (DS, TS, YZ, HC), pp. 1490–1496.

DAC-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.

DATE-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.

DATE-2015-LiaperdosSATAL #deployment #performance #using

Fast deployment of alternate analog test using Bayesian model fusion (JL, HGDS, LA, YT, AA, XL), pp. 1030–1035.

SIGMOD-2015-YangSN #correlation #difference #privacy

Bayesian Differential Privacy on Correlated Data (BY, IS, HN), pp. 747–762.

ICML-2015-BenavoliCMZ #algorithm #parametricity

A Bayesian nonparametric procedure for comparing algorithms (AB, GC, FM, MZ), pp. 1264–1272.

ICML-2015-DasBB #modelling #order #parametricity

Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models (MKD, TB, CB), pp. 550–559.

ICML-2015-Hernandez-Lobato15a #constraints #optimisation #predict

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints (JMHL, MAG, MWH, RPA, ZG), pp. 1699–1707.

ICML-2015-Hernandez-Lobato15b #learning #network #probability #scalability

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks (JMHL, RA), pp. 1861–1869.

ICML-2015-HugginsT

Risk and Regret of Hierarchical Bayesian Learners (JH, JT), pp. 1442–1451.

ICML-2015-KandasamySP #modelling #optimisation

High Dimensional Bayesian Optimisation and Bandits via Additive Models (KK, JGS, BP), pp. 295–304.

ICML-2015-KusnerGGW #optimisation

Differentially Private Bayesian Optimization (MJK, JRG, RG, KQW), pp. 918–927.

ICML-2015-MatthewCYW #empirical

Bayesian and Empirical Bayesian Forests (TM, CSC, JY, MW), pp. 967–976.

ICML-2015-RajanHSFJ #locality #multi

Bayesian Multiple Target Localization (PR, WH, RS, PIF, BJ), pp. 1945–1953.

ICML-2015-SamoR #parametricity #process #scalability

Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes (YLKS, SR), pp. 2227–2236.

ICML-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.

ICML-2015-Suzuki #convergence

Convergence rate of Bayesian tensor estimator and its minimax optimality (TS), pp. 1273–1282.

ICML-2015-WangZ #clustering #named #parametricity

DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics (YW, JZ), pp. 862–870.

ICML-2015-ZhaoMP #network #on the

On the Relationship between Sum-Product Networks and Bayesian Networks (HZ, MM, PP), pp. 116–124.

KDD-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.

KDD-2015-ScheinPBW #multi

Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts (AS, JWP, DMB, HMW), pp. 1045–1054.

KDD-2015-ZhangW #recommendation

A Collective Bayesian Poisson Factorization Model for Cold-start Local Event Recommendation (WZ, JW), pp. 1455–1464.

MLDM-2015-KrasotkinaM #approach #optimisation #ranking

A Bayesian Approach to Sparse Learning-to-Rank for Search Engine Optimization (OK, VM), pp. 382–394.

MLDM-2015-KrasotkinaM15a #analysis #approach

A Bayesian Approach to Sparse Cox Regression in High-Dimentional Survival Analysis (OK, VM), pp. 425–437.

RecSys-2015-GuoD #approach #bias #category theory

Uncovering Systematic Bias in Ratings across Categories: a Bayesian Approach (FG, DBD), pp. 317–320.

SIGIR-2015-GrotovWR #comparison #interactive

Bayesian Ranker Comparison Based on Historical User Interactions (AG, SW, MdR), pp. 273–282.

MoDELS-2015-LuddeckeSSS #modelling #network #using

Modeling user intentions for in-car infotainment systems using Bayesian networks (DL, CS, JS, IS), pp. 378–385.

OOPSLA-2015-OhYY #adaptation #learning #optimisation #program analysis

Learning a strategy for adapting a program analysis via bayesian optimisation (HO, HY, KY), pp. 572–588.

SAC-2015-Grossl #modelling #network

Modeling dependable systems with continuous time Bayesian networks (MG), pp. 436–441.

SAC-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.

SAC-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.

DAC-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.

DAC-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.

SIGMOD-2014-ZhangCPSX #named #network

PrivBayes: private data release via bayesian networks (JZ, GC, CMP, DS, XX), pp. 1423–1434.

DHM-2014-ZhangGBD #industrial #network

Application of Bayesian Networks in Consumer Service Industry and Healthcare (LZ, YG, BB, VGD), pp. 484–495.

ICEIS-v2-2014-LiuDT #network #reliability

Auditing Data Reliability in International Logistics — An Application of Bayesian Networks (LL, HAMD, RT), pp. 707–712.

ICML-c1-2014-LacosteMLL #learning

Agnostic Bayesian Learning of Ensembles (AL, MM, FL, HL), pp. 611–619.

ICML-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.

ICML-c1-2014-NguyenPNVB #clustering #multi #parametricity

Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts (TVN, DQP, XN, SV, HB), pp. 288–296.

ICML-c1-2014-ShiZ #learning #online

Online Bayesian Passive-Aggressive Learning (TS, JZ), pp. 378–386.

ICML-c2-2014-BaiLS #classification #framework #online

A Bayesian Framework for Online Classifier Ensemble (QB, HL, SS), pp. 1584–1592.

ICML-c2-2014-BarberW #difference #equation #estimation #process

Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations (DB, YW), pp. 1485–1493.

ICML-c2-2014-BenavoliCMZR #process

A Bayesian Wilcoxon signed-rank test based on the Dirichlet process (AB, GC, FM, MZ, FR), pp. 1026–1034.

ICML-c2-2014-Chapados #effectiveness #modelling

Effective Bayesian Modeling of Groups of Related Count Time Series (NC), pp. 1395–1403.

ICML-c2-2014-GardnerKZWC #constraints #difference #optimisation

Bayesian Optimization with Inequality Constraints (JRG, MJK, ZEX, KQW, JC), pp. 937–945.

ICML-c2-2014-JohnsonW #modelling #probability

Stochastic Variational Inference for Bayesian Time Series Models (MJ, ASW), pp. 1854–1862.

ICML-c2-2014-LiZ0 #learning #multi

Bayesian Max-margin Multi-Task Learning with Data Augmentation (CL, JZ, JC), pp. 415–423.

ICML-c2-2014-MinskerSLD #robust #scalability

Scalable and Robust Bayesian Inference via the Median Posterior (SM, SS, LL, DBD), pp. 1656–1664.

ICML-c2-2014-PentinaL #bound #learning

A PAC-Bayesian bound for Lifelong Learning (AP, CHL), pp. 991–999.

ICML-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.

ICML-c2-2014-SnoekSZA #optimisation

Input Warping for Bayesian Optimization of Non-Stationary Functions (JS, KS, RSZ, RPA), pp. 1674–1682.

ICPR-2014-ChamroukhiBG #clustering #parametricity

Bayesian Non-parametric Parsimonious Gaussian Mixture for Clustering (FC, MB, HG), pp. 1460–1465.

ICPR-2014-ChironGM #behaviour #parametricity #using

Discovering Emergent Behaviors from Tracks Using Hierarchical Non-parametric Bayesian Methods (GC, PGK, MM), pp. 2185–2190.

ICPR-2014-Filippone #classification #process #pseudo

Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMC (MF), pp. 614–619.

ICPR-2014-KumarG #documentation #keyword #learning

Bayesian Active Learning for Keyword Spotting in Handwritten Documents (GK, VG), pp. 2041–2046.

ICPR-2014-KunwarPB #network #online #recognition

Semi-supervised Online Bayesian Network Learner for Handwritten Characters Recognition (RK, UP, MB), pp. 3104–3109.

ICPR-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.

ICPR-2014-NguyenGVP #framework #parametricity #process #recognition #using

A Bayesian Nonparametric Framework for Activity Recognition Using Accelerometer Data (TN, SKG, SV, DQP), pp. 2017–2022.

ICPR-2014-NieJ #learning #linear #using

Feature Learning Using Bayesian Linear Regression Model (SN, QJ), pp. 1502–1507.

ICPR-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.

ICPR-2014-OsogamiK

A Hierarchical Bayesian Choice Model with Visibility (TO, TK), pp. 3618–3623.

ICPR-2014-QuLWXT #framework #robust #set

Robust Point Set Matching under Variational Bayesian Framework (HBQ, JCL, JQW, LX, HJT), pp. 58–63.

ICPR-2014-XingY #categorisation #image #parametricity #representation #scalability

Large Scale Image Categorization in Sparse Nonparametric Bayesian Representation (SX, NHCY), pp. 1365–1370.

ICPR-2014-XuS #learning #network #using

Bayesian Network Structure Learning Using Causality (ZX, SNS), pp. 3546–3551.

ICPR-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.

KDD-2014-EmbarPB #framework #network

A bayesian framework for estimating properties of network diffusions (VRE, RKP, IB), pp. 1216–1225.

KDD-2014-VasishtDVK #classification #learning #multi

Active learning for sparse bayesian multilabel classification (DV, ACD, MV, AK), pp. 472–481.

MLDM-2014-FuMD #classification #multi #network #performance #towards

Towards the Efficient Recovery of General Multi-Dimensional Bayesian Network Classifier (SF, SM, MCD), pp. 16–30.

MLDM-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.

MLDM-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.

RecSys-2014-KimC #collaboration #predict

Bayesian binomial mixture model for collaborative prediction with non-random missing data (YDK, SC), pp. 201–208.

RecSys-2014-LercheJ #feedback #personalisation #ranking #using

Using graded implicit feedback for bayesian personalized ranking (LL, DJ), pp. 353–356.

SIGIR-2014-CaiLR #documentation #matrix #personalisation #probability #ranking

Personalized document re-ranking based on Bayesian probabilistic matrix factorization (FC, SL, MdR), pp. 835–838.

SAC-2014-SeffrinRJ #algebra #network

A dynamic bayesian network for inference of learners’ algebraic knowledge (HMS, GLR, PAJ), pp. 235–240.

IJCAR-2014-CeylanP #logic

The Bayesian Description Logic ℬℰℒ (IIC, RP), pp. 480–494.

CASE-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.

DAC-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.

DAC-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.

ICDAR-2013-PuriST #learning #network

Bayesian Network Structure Learning and Inference Methods for Handwriting (MP, SNS, YT), pp. 1320–1324.

ICDAR-2013-RamaiahSG #framework #modelling

A Bayesian Framework for Modeling Accents in Handwriting (CR, AS, VG), pp. 917–921.

SIGMOD-2013-LinK #estimation #privacy #statistics

Information preservation in statistical privacy and bayesian estimation of unattributed histograms (BRL, DK), pp. 677–688.

DHM-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.

ICEIS-J-2013-LiL13a #object-oriented #predict

Bayesian Prediction of Fault-Proneness of Agile-Developed Object-Oriented System (LL, HL), pp. 209–225.

ICEIS-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.

ICML-c1-2013-AnandkumarHJK #learning #linear #network

Learning Linear Bayesian Networks with Latent Variables (AA, DH, AJ, SK), pp. 249–257.

ICML-c2-2013-WenKEB

Sequential Bayesian Search (ZW, BK, BE, SB), pp. 226–234.

ICML-c3-2013-BellemareVB #learning #recursion

Bayesian Learning of Recursively Factored Environments (MGB, JV, MB), pp. 1211–1219.

ICML-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.

ICML-c3-2013-GonenKK #kernel #matrix

Kernelized Bayesian Matrix Factorization (MG, SAK, SK), pp. 864–872.

ICML-c3-2013-GrosshansSBS #game studies #problem

Bayesian Games for Adversarial Regression Problems (MG, CS, MB, TS), pp. 55–63.

ICML-c3-2013-GuptaPV #approach #learning #multi #parametricity

Factorial Multi-Task Learning : A Bayesian Nonparametric Approach (SKG, DQP, SV), pp. 657–665.

ICML-c3-2013-LakshminarayananRT #top-down

Top-down particle filtering for Bayesian decision trees (BL, DMR, YWT), pp. 280–288.

ICML-c3-2013-PeharzTP #generative #network

The Most Generative Maximum Margin Bayesian Networks (RP, ST, FP), pp. 235–243.

KDD-2013-AhmedS #modelling #parametricity #scalability

The dataminer’s guide to scalable mixed-membership and nonparametric bayesian models (AA, AJS), p. 1529.

KDD-2013-FouldsBDSW #probability

Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation (JRF, LB, CD, PS, MW), pp. 446–454.

KDD-2013-NaganoITUA #modelling #parametricity

Nonparametric hierarchal bayesian modeling in non-contractual heterogeneous survival data (SN, YI, NT, TU, MA), pp. 668–676.

RecSys-2013-BabasCT #personalisation #recommendation #what

You are what you consume: a bayesian method for personalized recommendations (KB, GC, ET), pp. 221–228.

SEKE-2013-DuttaAKB #approach #distributed

Virtual Medical Board: A Distributed Bayesian Agent Based Approach (S) (AD, SA, AK, SB), pp. 685–688.

POPL-2013-GordonABCGNRR #reasoning

A model-learner pattern for bayesian reasoning (ADG, MA, JB, GC, TG, AVN, SKR, CVR), pp. 403–416.

ESEC-FSE-2013-ClaretRNGB #analysis #data flow #using

Bayesian inference using data flow analysis (GC, SKR, AVN, ADG, JB), pp. 92–102.

CASE-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.

VLDB-2012-SatuluriP #locality #performance #similarity

Bayesian Locality Sensitive Hashing for Fast Similarity Search (VS, SP), pp. 430–441.

VLDB-2012-ZhaoRGH #approach #integration

A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration (BZ, BIPR, JG, JH), pp. 550–561.

STOC-2012-BeiCGL #design

Budget feasible mechanism design: from prior-free to bayesian (XB, NC, NG, PL), pp. 449–458.

ICEIS-v1-2012-NikovskiEYST #automation #composition #network

Bayesian Networks for Matcher Composition in Automatic Schema Matching (DN, AE, XY, MS, ST), pp. 48–55.

CIKM-2012-JiaZH #network #simulation

Non-stationary bayesian networks based on perfect simulation (YJ, WZ, JH), pp. 1095–1104.

CIKM-2012-XuZG #multi #online

Multiview hierarchical bayesian regression model andapplication to online advertising (TX, RZ, ZG), pp. 485–494.

ICML-2012-AhnBW #probability

Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring (SA, AKB, MW), p. 230.

ICML-2012-AzimiJF #hybrid #optimisation

Hybrid Batch Bayesian Optimization (JA, AJ, XZF), p. 45.

ICML-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.

ICML-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.

ICML-2012-BracegirdleB

Bayesian Conditional Cointegration (CB, DB), p. 220.

ICML-2012-DundarAQR #learning #modelling #online

Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes (MD, FA, AQ, BR), p. 18.

ICML-2012-FujimakiH #markov #modelling

Factorized Asymptotic Bayesian Hidden Markov Models (RF, KH), p. 157.

ICML-2012-GarnettKXSM

Bayesian Optimal Active Search and Surveying (RG, YK, XX, JGS, RPM), p. 111.

ICML-2012-Gonen #kernel #learning #multi #performance

Bayesian Efficient Multiple Kernel Learning (MG), p. 17.

ICML-2012-KoS #modelling #scalability

Large Scale Variational Bayesian Inference for Structured Scale Mixture Models (YJK, MWS), p. 229.

ICML-2012-KulisJ #algorithm

Revisiting k-means: New Algorithms via Bayesian Nonparametrics (BK, MIJ), p. 148.

ICML-2012-MohamedHG #learning

Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning (SM, KAH, ZG), p. 91.

ICML-2012-MorvantKR #bound #classification #matrix #multi

PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification (EM, SK, LR), p. 158.

ICML-2012-PaisleyBJ #probability

Variational Bayesian Inference with Stochastic Search (JWP, DMB, MIJ), p. 177.

ICML-2012-PeharzP #learning #network

Exact Maximum Margin Structure Learning of Bayesian Networks (RP, FP), p. 102.

ICML-2012-ShterevD

Bayesian Watermark Attacks (IS, DBD), p. 153.

ICML-2012-WangWHL #learning #monte carlo

Monte Carlo Bayesian Reinforcement Learning (YW, KSW, DH, WSL), p. 105.

ICML-2012-XuYQ #composition #data analysis #infinity #modelling #multi #parametricity

Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis (ZX, FY, AQ), p. 218.

ICML-2012-ZhongG #approach #approximate #matrix

A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices (MZ, MAG), p. 87.

ICPR-2012-Capitaine #equivalence #probability

Set-valued Bayesian inference with probabilistic equivalence (HLC), pp. 2132–2135.

ICPR-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.

ICPR-2012-GuptaPV #parametricity

A nonparametric Bayesian Poisson gamma model for count data (SKG, DQP, SV), pp. 1815–1818.

ICPR-2012-KafaiBA #classification #clustering #estimation #network

Cluster-Classification Bayesian Networks for head pose estimation (MK, BB, LA), pp. 2869–2872.

ICPR-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.

ICPR-2012-TianC #image #video

Bayesian image enlargement for mixed-resolution video (JT, LC), pp. 3082–3085.

ICPR-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.

ICPR-2012-WangJ #network #recognition

Incorporating contextual knowledge to Dynamic Bayesian Networks for event recognition (XW, QJ), pp. 3378–3381.

ICPR-2012-WangJ12b #learning #network #process #recognition

Learning dynamic Bayesian network discriminatively for human activity recognition (XW, QJ), pp. 3553–3556.

ICPR-2012-YoonFW #generative

Bayesian separation of wind power generation signals (JWY, FF, MW), pp. 2660–2663.

ICPR-2012-YoonT #implementation

Bayesian implementation of a Lagrangian macroscopic traffic flow model (JWY, TTT), pp. 214–217.

ICPR-2012-ZhangWN #detection #feature model #student

Bayesian feature selection and model detection for student’s t-mixture distributions (HZ, QMJW, TMN), pp. 1631–1634.

ICPR-2012-ZhaoXY #learning #network #speech

Unsupervised Tibetan speech features Learning based on Dynamic Bayesian Networks (YZ, XX, GY), pp. 2319–2322.

KDD-2012-SilvaC #learning #matrix #online

Active learning for online bayesian matrix factorization (JGS, LC), pp. 325–333.

KDD-2012-Ueda #data analysis #relational

Bayesian relational data analysis (NU), p. 815.

KDIR-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.

KDIR-2012-CheungZZ #approach #network

A Bayesian Approach for Constructing Ensemble Neural Network (SHC, YZ, ZZ), pp. 374–377.

KMIS-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.

MLDM-2012-BouhamedMLR #heuristic #learning #network

A New Learning Structure Heuristic of Bayesian Networks from Data (HB, AM, TL, AR), pp. 183–197.

MLDM-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.

RecSys-2012-Herbrich #distributed #learning #online #realtime

Distributed, real-time bayesian learning in online services (RH), pp. 203–204.

SEKE-2012-PittoliSN #monitoring #network

Investigating the Use of Bayesian Networks as a Support Tool for Monitoring Software Projects (FP, ALRdS, DJN), pp. 570–573.

SIGIR-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.

SAC-2012-JabeurTB #microblog #network #retrieval #twitter

Uprising microblogs: a bayesian network retrieval model for tweet search (LBJ, LT, MB), pp. 943–948.

CBSE-2011-AletiM #component #deployment #learning #optimisation

Component deployment optimisation with bayesian learning (AA, IM), pp. 11–20.

CASE-2011-KurzKP #maintenance #network #using

Dynamic Maintenance in semiconductor manufacturing using Bayesian networks (DK, JK, JP), pp. 238–243.

DAC-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.

ICDAR-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.

ICDAR-2011-ShahabSD #approach #recognition

Bayesian Approach to Photo Time-Stamp Recognition (AS, FS, AD), pp. 1039–1043.

ESOP-2011-BorgstromGGMG #machine learning #semantics

Measure Transformer Semantics for Bayesian Machine Learning (JB, ADG, MG, JM, JVG), pp. 77–96.

DHM-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.

DHM-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.

ICEIS-v2-2011-ZhuXM #assessment #network #risk management #using

Operational Hazard Risk Assessment using Bayesian Networks (ZJZ, YX, EM), pp. 135–139.

ICEIS-v4-2011-HuGK #evaluation #modelling #network #trust

Supply Chain Trust Evaluation Model based on Bayesian Network (XH, FG, YK), pp. 712–715.

CIKM-2011-HarveyCRC #collaboration #modelling #predict #rating

Bayesian latent variable models for collaborative item rating prediction (MH, MJC, IR, FC), pp. 699–708.

CIKM-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.

CIKM-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.

ICML-2011-BarthelmeC #named

ABC-EP: Expectation Propagation for Likelihoodfree Bayesian Computation (SB, NC), pp. 289–296.

ICML-2011-DoshiWTR #infinity #network

Infinite Dynamic Bayesian Networks (FD, DW, JBT, NR), pp. 913–920.

ICML-2011-NakajimaSB #automation #on the

On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution (SN, MS, SDB), pp. 497–504.

ICML-2011-NikolenkoS #contest #rating

A New Bayesian Rating System for Team Competitions (SIN, AS), pp. 601–608.

ICML-2011-VirtanenKK

Bayesian CCA via Group Sparsity (SV, AK, SK), pp. 457–464.

ICML-2011-WellingT #learning #probability

Bayesian Learning via Stochastic Gradient Langevin Dynamics (MW, YWT), pp. 681–688.

KDD-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.

RecSys-2011-BarbieriCMO #approach #modelling #recommendation

Modeling item selection and relevance for accurate recommendations: a bayesian approach (NB, GC, GM, RO), pp. 21–28.

SAC-2011-YeHL #approach #network #query

A Bayesian network approach to context sensitive query expansion (ZY, XH, HL), pp. 1138–1142.

ICLP-2011-ChristiansenHLP #analysis #network #sequence

Bayesian Annotation Networks for Complex Sequence Analysis (HC, CTH, OTL, MP), pp. 220–230.

DAC-2010-ZhangLR

Bayesian virtual probe: minimizing variation characterization cost for nanoscale IC technologies via Bayesian inference (WZ, XL, RAR), pp. 262–267.

DATE-2010-KrishnanDBK

Block-level bayesian diagnosis of analogue electronic circuits (SK, KDD, RB, HGK), pp. 1767–1772.

STOC-2010-HartlineL #algorithm #design

Bayesian algorithmic mechanism design (JDH, BL), pp. 301–310.

IFL-2010-TorontoM #probability

From Bayesian Notation to Pure Racket via Discrete Measure-Theoretic Probability in λ ZFC (NT, JM), pp. 89–104.

ICEIS-AIDSS-2010-AhdabG #learning #network #performance

Efficient Learning of Dynamic Bayesian Networks from Timed Data (AA, MLG), pp. 226–231.

ICEIS-AIDSS-2010-TroianoP #complexity #modelling

Supporting Complexity in Modeling Bayesian Troubleshooting (LT, DDP), pp. 344–349.

CIKM-2010-ZhangWWCZHZ #learning #modelling

Learning click models via probit bayesian inference (YZ, DW, GW, WC, ZZ, BH, LZ), pp. 439–448.

ICML-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.

ICML-2010-DowneyS #adaptation #difference

Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting λ (CD, SS), pp. 311–318.

ICML-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.

ICML-2010-HoffmanBC #matrix #music #parametricity

Bayesian Nonparametric Matrix Factorization for Recorded Music (MDH, DMB, PRC), pp. 439–446.

ICML-2010-LazaricG #learning #multi

Bayesian Multi-Task Reinforcement Learning (AL, MG), pp. 599–606.

ICML-2010-LiangJK #approach #learning #source code

Learning Programs: A Hierarchical Bayesian Approach (PL, MIJ, DK), pp. 639–646.

ICML-2010-NakajimaS #matrix

Implicit Regularization in Variational Bayesian Matrix Factorization (SN, MS), pp. 815–822.

ICPR-2010-Bouchaffra #network

Topological Dynamic Bayesian Networks (DB), pp. 898–901.

ICPR-2010-CamposZJ

An Improved Structural EM to Learn Dynamic Bayesian Nets (CPdC, ZZ, QJ), pp. 601–604.

ICPR-2010-KirbizCG #matrix #modelling

Bayesian Inference for Nonnegative Matrix Factor Deconvolution Models (SK, ATC, BG), pp. 2812–2815.

ICPR-2010-PhilippotBB #algorithm #classification #learning #network #online

Bayesian Networks Learning Algorithms for Online Form Classification (EP, YB, AB), pp. 1981–1984.

ICPR-2010-PiroNNB #classification

Boosting Bayesian MAP Classification (PP, RN, FN, MB), pp. 661–665.

ICPR-2010-PuS #learning #probability #verification

Probabilistic Measure for Signature Verification Based on Bayesian Learning (DP, SNS), pp. 1188–1191.

ICPR-2010-Tagawa #eye tracking #modelling #statistics #using

Depth Perception Model Based on Fixational Eye Movements Using Bayesian Statistical Inference (NT), pp. 1662–1665.

ICPR-2010-TakasuFA #algorithm #similarity

A Variational Bayesian EM Algorithm for Tree Similarity (AT, DF, TA), pp. 1056–1059.

ICPR-2010-TanveerI #approach #using

A Bayesian Approach to Face Hallucination Using DLPP and KRR (MT, NI), pp. 2154–2157.

ICPR-2010-UyarBCB #development #network #predict

Bayesian Networks for Predicting IVF Blastocyst Development (AU, ABB, HNC, MB), pp. 2772–2775.

ICPR-2010-Wang #detection #documentation #recognition #using

Document Logo Detection and Recognition Using Bayesian Model (HW), pp. 1961–1964.

ICPR-2010-WirkertDC

Bayesian GOETHE Tracking (SW, ED, LC), pp. 2077–2080.

ICPR-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.

SEKE-2010-BackerM #algorithm #network

Evaluating the Weighted Sum Algorithm for Estimating Conditional Probabilities in Bayesian Networks (SB, EM), pp. 319–324.

SEKE-2010-GoelXS #multi #network #online #verification

A Multi-State Bayesian Network for Shill Verification in Online Auctions (AG, HX, SMS), pp. 279–285.

SEKE-2010-Radlinski #development #predict #quality #using

Software Development Effort and Quality Prediction Using Bayesian Nets and small Local Qualitative Data (LR), pp. 113–116.

SAC-2010-AyyappanWN #algorithm #constraints #learning #named #network #scalability

MICHO: a scalable constraint-based algorithm for learning Bayesian networks (MA, YKW, WKN), pp. 985–989.

SAC-2010-LeeKL #network #personalisation #taxonomy

Applying taxonomic knowledge to Bayesian belief network for personalized search (JwL, HjK, SgL), pp. 1796–1801.

SAC-2010-QinXL #classification #nondeterminism

A Bayesian classifier for uncertain data (BQ, YX, FL), pp. 1010–1014.

ICSE-2010-SridharanN #data analysis #re-engineering

Bayesian methods for data analysis in software engineering (MS, ASN), pp. 477–478.

CASE-2009-FanL #effectiveness #ranking

A Bayesian Ranking Scheme for supporting cost-effective yield diagnosis services (CMF, YPL), pp. 427–432.

DRR-2009-Likforman-SulemS #classification #network #recognition

Combination of dynamic Bayesian network classifiers for the recognition of degraded characters (LLS, MS), pp. 1–10.

ICDAR-2009-BarratT #image #modelling #network #using

Modeling, Classifying and Annotating Weakly Annotated Images Using Bayesian Network (SB, ST), pp. 1201–1205.

ICDAR-2009-HamamuraAI09a #pattern matching #pattern recognition #recognition

Bayesian Best-First Search for Pattern Recognition — Application to Address Recognition (TH, TA, BI), pp. 461–465.

ICDAR-2009-LuqmanBR #classification #graph #network #recognition #using

Graphic Symbol Recognition Using Graph Based Signature and Bayesian Network Classifier (MML, TB, JYR), pp. 1325–1329.

ICDAR-2009-StefanoFFM #classification #evolution #learning #network

Learning Bayesian Networks by Evolution for Classifier Combination (CDS, FF, ASdF, AM), pp. 966–970.

ICDAR-2009-Takasu #approximate #estimation #similarity

Bayesian Similarity Model Estimation for Approximate Recognized Text Search (AT), pp. 611–615.

VLDB-2009-MozafariZ #classification #naive bayes #privacy

Publishing Naive Bayesian Classifiers: Privacy without Accuracy Loss (BM, CZ), pp. 1173–1185.

ITiCSE-2009-KasurinenN #programming

Estimating programming knowledge with Bayesian knowledge tracing (JK, UN), pp. 313–317.

CSMR-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.

DHM-2009-MobusE #approach #modelling #programming #towards

Further Steps towards Driver Modeling According to the Bayesian Programming Approach (CM, ME), pp. 413–422.

CIKM-2009-KuoCW #learning #rank

Learning to rank from Bayesian decision inference (JWK, PJC, HMW), pp. 827–836.

CIKM-2009-MasadaFTHSO #analysis #optimisation #topic

Dynamic hyperparameter optimization for bayesian topical trend analysis (TM, DF, AT, TH, YS, KO), pp. 1831–1834.

ECIR-2009-StathopoulosJ #automation #image

Bayesian Mixture Hierarchies for Automatic Image Annotation (VS, JMJ), pp. 138–149.

ICML-2009-AdamsG #learning #named #parametricity

Archipelago: nonparametric Bayesian semi-supervised learning (RPA, ZG), pp. 1–8.

ICML-2009-AdamsMM #parametricity #process

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities (RPA, IM, DJCM), pp. 9–16.

ICML-2009-CamposZJ #constraints #learning #network #using

Structure learning of Bayesian networks using constraints (CPdC, ZZ, QJ), pp. 113–120.

ICML-2009-GarnettOR #predict

Sequential Bayesian prediction in the presence of changepoints (RG, MAO, SJR), pp. 345–352.

ICML-2009-GermainLLM #classification #learning #linear

PAC-Bayesian learning of linear classifiers (PG, AL, FL, MM), pp. 353–360.

ICML-2009-GuiverS #modelling #ranking

Bayesian inference for Plackett-Luce ranking models (JG, ES), pp. 377–384.

ICML-2009-HaiderS #clustering #detection #email

Bayesian clustering for email campaign detection (PH, TS), pp. 385–392.

ICML-2009-KamisettyL #approach #assessment #quality

A Bayesian approach to protein model quality assessment (HK, CJL), pp. 481–488.

ICML-2009-KolterN #polynomial

Near-Bayesian exploration in polynomial time (JZK, AYN), pp. 513–520.

ICML-2009-NickischS #linear #modelling #scalability

Convex variational Bayesian inference for large scale generalized linear models (HN, MWS), pp. 761–768.

ICML-2009-RamanFWDR

The Bayesian group-Lasso for analyzing contingency tables (SR, TJF, PJW, ED, VR), pp. 881–888.

KDD-2009-LiuGF #named

BBM: bayesian browsing model from petabyte-scale data (CL, FG, CF), pp. 537–546.

MLDM-2009-GaagRFGEBF #classification #network

Aligning Bayesian Network Classifiers with Medical Contexts (LCvdG, SR, AF, AdG, MJCE, FJB, BCJMF), pp. 787–801.

SIGIR-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.

SAC-2009-Ries #modelling #representation #trust

Extending Bayesian trust models regarding context-dependence and user friendly representation (SR), pp. 1294–1301.

SAC-2009-SherminO #network #using

Using dynamic bayesian networks to infer gene regulatory networks from expression profiles (AS, MAO), pp. 799–803.

SAC-2009-Villamarin-SalomonB #detection #similarity

Bayesian bot detection based on DNS traffic similarity (RVS, JCB), pp. 2035–2041.

WCRE-2008-ZhouWGGL #approach #network #predict

A Bayesian Network Based Approach for Change Coupling Prediction (YZ, MW, EG, HG, JL), pp. 27–36.

ICALP-A-2008-ChristodoulouKS #combinator

Bayesian Combinatorial Auctions (GC, AK, MS), pp. 820–832.

ICEIS-AIDSS-2008-BringasPPS

Bayesian-Networks-Based Misuse and Anomaly Prevention System (PGB, YKP, SP, PS), pp. 62–69.

ICML-2008-CaronD #parametricity

Sparse Bayesian nonparametric regression (FC, AD), pp. 88–95.

ICML-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.

ICML-2008-ReisingerSM #kernel #learning #online

Online kernel selection for Bayesian reinforcement learning (JR, PS, RM), pp. 816–823.

ICML-2008-SalakhutdinovM08a #markov #matrix #monte carlo #probability #using

Bayesian probabilistic matrix factorization using Markov chain Monte Carlo (RS, AM), pp. 880–887.

ICML-2008-SeegerN #design

Compressed sensing and Bayesian experimental design (MWS, HN), pp. 912–919.

ICML-2008-SuZLM #learning #network #parametricity

Discriminative parameter learning for Bayesian networks (JS, HZ, CXL, SM), pp. 1016–1023.

ICPR-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.

ICPR-2008-Boccignone #analysis #parametricity #video

Nonparametric Bayesian attentive video analysis (GB), pp. 1–4.

ICPR-2008-CamposJ #constraints #learning #network #parametricity #using

Improving Bayesian Network parameter learning using constraints (CPdC, QJ), pp. 1–4.

ICPR-2008-ChenTZ #classification #novel

Spam filtering with several novel bayesian classifiers (CC, YT, CZ), pp. 1–4.

ICPR-2008-Dahyot #classification #statistics

Bayesian classification for the Statistical Hough transform (RD), pp. 1–4.

ICPR-2008-HeAP

A Bayesian Local Binary Pattern texture descriptor (CH, TA, MP), pp. 1–4.

ICPR-2008-LiaoJ #learning #network #parametricity #semistructured data

Exploiting qualitative domain knowledge for learning Bayesian network parameters with incomplete data (WL, QJ), pp. 1–4.

ICPR-2008-MatsuiCM #automation #detection

Bayesian sequential face detection with automatic re-initialization (AM, SC, TM), pp. 1–4.

ICPR-2008-SukSL #gesture #modelling #network #recognition #robust

Robust modeling and recognition of hand gestures with dynamic Bayesian network (HIS, BKS, SWL), pp. 1–4.

ICPR-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.

KDD-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.

KDD-2008-SongJRG #linear

A bayesian mixture model with linear regression mixing proportions (XS, CJ, SR, JG), pp. 659–667.

SEKE-2008-CemerlicYK #detection #network

Network Intrusion Detection Based on Bayesian Networks (AC, LY, JMK), pp. 791–794.

SIGIR-2008-XuA #feedback

A bayesian logistic regression model for active relevance feedback (ZX, RA), pp. 227–234.

ICST-2008-MirarabT #approach #empirical #testing

An Empirical Study on Bayesian Network-based Approach for Test Case Prioritization (SM, LT), pp. 278–287.

QoSA-2007-RoshandelMG #architecture #predict #reliability

A Bayesian Model for Predicting Reliability of Software Systems at the Architectural Level (RR, NM, LG), pp. 108–126.

CASE-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.

DRR-2007-Likforman-SulemS #network #recognition #using

Recognition of degraded handwritten digits using dynamic Bayesian networks (LLS, MS).

ICDAR-2007-Likforman-SulemS #network #recognition #using

Recognition of Broken Characters from Historical Printed Books Using Dynamic Bayesian Networks (LLS, MS), pp. 173–177.

ICDAR-2007-WillemsV #approach #detection #interactive #network

A Bayesian Network Approach to Mode Detection for Interactive Maps (DW, LV), pp. 869–873.

VLDB-2007-SilbersteinGMPY #approach

Making Sense of Suppressions and Failures in Sensor Data: A Bayesian Approach (AS, AEG, KM, GP, JY), pp. 842–853.

VLDB-2007-WuJ #set

A Bayesian Method for Guessing the Extreme Values in a Data Set (MW, CJ), pp. 471–482.

FASE-2007-MirarabT #approach #network #testing

A Prioritization Approach for Software Test Cases Based on Bayesian Networks (SM, LT), pp. 276–290.

ICPC-2007-MirarabHT #co-evolution #network #predict #using

Using Bayesian Belief Networks to Predict Change Propagation in Software Systems (SM, AH, LT), pp. 177–188.

HIMI-MTT-2007-AblassmeierPRR #network #using

Context-Aware Information Agents for the Automotive Domain Using Bayesian Networks (MA, TP, SR, GR), pp. 561–570.

HIMI-MTT-2007-LinL #analysis #automation

A Bayesian Methodology for Semi-automated Task Analysis (SCL, MRL), pp. 697–704.

ICEIS-AIDSS-2007-StateCPS #approach #classification

A Connectionist Approach in Bayesian Classification (LS, CC, PV, VS), pp. 185–190.

CIKM-2007-LiuTZ #learning #network

Ensembling Bayesian network structure learning on limited data (FL, FT, QZ), pp. 927–930.

ECIR-2007-YeungBCK #approach #documentation #learning

A Bayesian Approach for Learning Document Type Relevance (PCKY, SB, CLAC, MK), pp. 753–756.

ICML-2007-GhavamzadehE #algorithm

Bayesian actor-critic algorithms (MG, YE), pp. 297–304.

ICML-2007-Jaeger #learning #network #parametricity #relational

Parameter learning for relational Bayesian networks (MJ), pp. 369–376.

ICML-2007-JiC #optimisation

Bayesian compressive sensing and projection optimization (SJ, LC), pp. 377–384.

ICML-2007-KropotovV #learning #on the

On one method of non-diagonal regularization in sparse Bayesian learning (DK, DV), pp. 457–464.

ICML-2007-TitovH #incremental #network #predict

Incremental Bayesian networks for structure prediction (IT, JH), pp. 887–894.

ICML-2007-WilsonFRT #approach #learning #multi

Multi-task reinforcement learning: a hierarchical Bayesian approach (AW, AF, SR, PT), pp. 1015–1022.

ICML-2007-YamazakiKWSM #fault

Asymptotic Bayesian generalization error when training and test distributions are different (KY, MK, SW, MS, KRM), pp. 1079–1086.

MLDM-2007-Holness #network

A Direct Measure for the Efficacy of Bayesian Network Structures Learned from Data (GH), pp. 601–615.

RecSys-2007-PronkVPT #classification #naive bayes #recommendation

Incorporating user control into recommender systems based on naive bayesian classification (VP, WFJV, AP, MT), pp. 73–80.

SIGIR-2007-ZhangK #modelling #performance #recommendation

Efficient bayesian hierarchical user modeling for recommendation system (YZ, JK), pp. 47–54.

SAC-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.

CIKM-2006-ZigorisZ #adaptation #feedback #profiling

Bayesian adaptive user profiling with explicit & implicit feedback (PZ, YZ), pp. 397–404.

ECIR-2006-Amati #approach #information retrieval

Frequentist and Bayesian Approach to Information Retrieval (GA), pp. 13–24.

ICML-2006-Banerjee #bound #on the

On Bayesian bounds (AB), pp. 81–88.

ICML-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.

ICML-2006-LeeGW #classification #using

Using query-specific variance estimates to combine Bayesian classifiers (CHL, RG, SW), pp. 529–536.

ICML-2006-PoupartVHR #learning

An analytic solution to discrete Bayesian reinforcement learning (PP, NAV, JH, KR), pp. 697–704.

ICML-2006-SilvaS #learning #metric #modelling

Bayesian learning of measurement and structural models (RBdAeS, RS), pp. 825–832.

ICML-2006-SternHG #game studies #predict #ranking

Bayesian pattern ranking for move prediction in the game of Go (DHS, RH, TG), pp. 873–880.

ICML-2006-SuZ #classification #network

Full Bayesian network classifiers (JS, HZ), pp. 897–904.

ICML-2006-TingDS

Bayesian regression with input noise for high dimensional data (JAT, AD, SS), pp. 937–944.

ICML-2006-XingSJT #multi #process #type inference

Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture (EPX, KAS, MIJ, YWT), pp. 1049–1056.

ICPR-v1-2006-CarterYF #approach #behaviour #markov #recognition

A Combined Bayesian Markovian Approach for Behaviour Recognition (NLC, DPY, JMF), pp. 761–764.

ICPR-v1-2006-DuCXL #interactive #network #process #using

Recognizing Interaction Activities using Dynamic Bayesian Network (YD, FC, WX, YL), pp. 618–621.

ICPR-v1-2006-GormanTBH #behaviour #game studies #interactive

Bayesian Imitation of Human Behavior in Interactive Computer Games (BG, CT, CB, MH), pp. 1244–1247.

ICPR-v1-2006-RiusVGV #performance

Action Spaces for Efficient Bayesian Tracking of Human Motion (IR, XV, JG, JJV), pp. 472–475.

ICPR-v1-2006-WongC #classification #gesture #recognition #using

Continuous Gesture Recognition using a Sparse Bayesian Classifier (SFW, RC), pp. 1084–1087.

ICPR-v1-2006-ZhouH #network #visual notation

Weighted Bayesian Network for Visual Tracking (YZ, TSH), pp. 523–526.

ICPR-v2-2006-ChangYP #approach

An Iterative Bayesian Approach for Digital Matting (HC, QY, CP), pp. 122–125.

ICPR-v2-2006-LernerM #classification #image #learning #network

Learning Bayesian Networks for Cytogenetic Image Classification (BL, RM), pp. 772–775.

ICPR-v2-2006-McDanielKP #approach #classification #visual notation

A Bayesian Approach to Visual Size Classification of Everyday Objects (TLM, KK, SP), pp. 255–259.

ICPR-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.

ICPR-v3-2006-HarmoucheCAFA #classification #modelling

Bayesian MS Lesion Classification Modeling Regional and Local Spatial Information (RH, DLC, DLA, SJF, TA), pp. 984–987.

ICPR-v3-2006-JainML #clustering #feedback

Bayesian Feedback in Data Clustering (AKJ, PKM, MHCL), pp. 374–378.

ICPR-v3-2006-MaKKLK #estimation

Sparse Bayesian Regression for Head Pose Estimation (YM, YK, KK, SL, MK), pp. 507–510.

ICPR-v3-2006-MatosC #classification #network

Combining global and local classifiers with Bayesian network (LNM, JMdC), pp. 1212–1215.

ICPR-v3-2006-PalenichkaZ #image #network #using

Perceptual Knowledge Extraction Using Bayesian Networks of Salient Image Objects (RMP, MBZ), pp. 1216–1219.

ICPR-v3-2006-WongWC #classification #robust #using

Robust Appearance-based Tracking using a sparse Bayesian classifier (SFW, KYKW, RC), pp. 47–50.

ICPR-v4-2006-LinO #network #recognition #speech

Switching Auxiliary Chains for Speech Recognition based on Dynamic Bayesian Networks (HL, ZO), pp. 258–261.

ICPR-v4-2006-LiuH #automation #predict #segmentation #speech

A Bayesian Predictive Method for Automatic Speech Segmentation (ML, TSH), pp. 290–293.

ICPR-v4-2006-MatosC06a #classification #network

Combining global and local classifiers with Bayesian network (LNM, JMdC), p. 952.

ICPR-v4-2006-PalenichkaZ06a #image #network #using

Perceptual Knowledge Extraction Using Bayesian Networks of Salient Image Objects (RMP, MBZ), p. 953.

ICPR-v4-2006-WangK #approach #automation #estimation #using

Automatic Alignment of High-Resolution NMR Spectra Using a Bayesian Estimation Approach (ZW, SBK), pp. 667–670.

SEKE-2006-HaiderC #estimation #fault

Bayesian Estimation of Defects based on Defect Decay Model: BayesED3M (SWH, JWC), pp. 256–261.

SAC-2006-MerschmannP #approach #classification

A Bayesian approach for protein classification (LHdCM, AP), pp. 200–201.

ICSE-2006-AbeMKKH #classification #estimation #using

Estimation of project success using Bayesian classifier (SA, OM, TK, NK, MH), pp. 600–603.

ICSE-2006-MandelinKY #approach #architecture #diagrams #modelling

A Bayesian approach to diagram matching with application to architectural models (DM, DK, DMY), pp. 222–231.

WICSA-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.

ICDAR-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.

SAS-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.

ICEIS-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.

ICML-2005-AngelopoulosC

Tempering for Bayesian C&RT (NA, JC), pp. 17–24.

ICML-2005-BurgeL #learning #network

Learning class-discriminative dynamic Bayesian networks (JB, TL), pp. 97–104.

ICML-2005-ChengJSW #image #modelling

Variational Bayesian image modelling (LC, FJ, DS, SW), pp. 129–136.

ICML-2005-GirolamiR #kernel #learning #modelling

Hierarchic Bayesian models for kernel learning (MG, SR), pp. 241–248.

ICML-2005-HellerG #clustering

Bayesian hierarchical clustering (KAH, ZG), pp. 297–304.

ICML-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.

ICML-2005-KoivistoS #aspect-oriented #modelling

Computational aspects of Bayesian partition models (MK, KS), pp. 433–440.

ICML-2005-PernkopfB #classification #generative #learning #network #parametricity

Discriminative versus generative parameter and structure learning of Bayesian network classifiers (FP, JAB), pp. 657–664.

ICML-2005-SnelsonG #approximate #predict

Compact approximations to Bayesian predictive distributions (ES, ZG), pp. 840–847.

ICML-2005-WangLBS #online #optimisation

Bayesian sparse sampling for on-line reward optimization (TW, DJL, MHB, DS), pp. 956–963.

KDD-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.

KDD-2005-JaroszewiczS #network #performance

Fast discovery of unexpected patterns in data, relative to a Bayesian network (SJ, TS), pp. 118–127.

MLDM-2005-KarrasMGO #mining

Improved MRI Mining by Integrating Support Vector Machine Priors in the Bayesian Restoration (DAK, BGM, DGD, DvO), pp. 325–333.

SEKE-2005-LienT #network #recommendation #web

A Web Pages Recommender with Bayesian Networks (CCL, HLT), pp. 82–87.

ICEIS-v2-2004-BendouM #graph #learning #network

Learning Bayesian Networks with Largest Chain Graphs (MB, PM), pp. 184–190.

ICEIS-v2-2004-ColaceSVF #algorithm #automation #learning #ontology

A Semi-Automatic Bayesian Algorithm for Ontology Learning (FC, MDS, MV, PF), pp. 191–196.

ICEIS-v2-2004-ColaceSVF04a #algorithm #comparison #learning #network

Bayesian Network Structural Learning from Data: An Algorithms Comparison (FC, MDS, MV, PF), pp. 527–530.

ICML-2004-DSouzaVS

The Bayesian backfitting relevance vector machine (AD, SV, SS).

ICML-2004-GrossmanD #classification #learning #network

Learning Bayesian network classifiers by maximizing conditional likelihood (DG, PMD).

ICML-2004-XingSJ #process #type inference

Bayesian haplo-type inference via the dirichlet process (EPX, RS, MIJ).

ICML-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).

ICPR-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.

ICPR-v1-2004-LombardiZ #architecture #design

Architectural Design Issues for Bayesian Contextual Vision (PL, BZ), pp. 753–756.

ICPR-v1-2004-PereraH #detection

Bayesian Object-Level Change Detection in Grayscale Imagery (AGAP, AH), pp. 71–75.

ICPR-v1-2004-SunG #image #markov #random #segmentation

Bayesian Image Segmentation Based on an Inhomogeneous Hidden Markov Random Field (JS, DG), pp. 596–599.

ICPR-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.

ICPR-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.

ICPR-v2-2004-KagehiroKSF

Address-Block Extraction by Bayesian Rule (TK, MK, HS, HF), pp. 582–585.

ICPR-v2-2004-KaneS #classification #image #learning #network

Bayesian Network Structure Learning and Inference in Indoor vs. Outdoor Image Classification (MJK, AES), pp. 479–482.

ICPR-v2-2004-Nagao #approach #feature model #kernel

Bayesian Approach with Nonlinear Kernels to Feature Extraction (KN), pp. 153–156.

ICPR-v2-2004-SebeCHG #approach #detection #network

Skin Detection: A Bayesian Network Approach (NS, IC, TSH, TG), pp. 903–906.

ICPR-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.

ICPR-v2-2004-ZhangZ #automation #concept #framework #image

A Bayesian Framework for Automatic Concept Discovery in Image Collections (RZ, Z(Z), pp. 973–976.

ICPR-v3-2004-AnsaryVD #3d #approach #modelling #retrieval

A Bayesian Approach for 3D Models Retrieval Based on Characteristic Views (TFA, JPV, MD), pp. 898–901.

ICPR-v3-2004-GurwiczL #agile #estimation #kernel #network

Rapid Spline-based Kernel Density Estimation for Bayesian Networks (YG, BL), pp. 700–703.

ICPR-v3-2004-LifshitsBGRR #geometry

Rehashing for Bayesian Geometric Hashing (ML, IB, RG, ER, MR), pp. 99–102.

ICPR-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.

ICPR-v4-2004-BoubchirFB #approximate #using

Bayesian Denoising in the Wavelet-Domain Using an Analytical Approximate a-Stable Prior (LB, MJF, DB), pp. 889–892.

ICPR-v4-2004-ChantasGL #image

Non Stationary Bayesian Image Restoration (GKC, NPG, AL), pp. 689–692.

ICPR-v4-2004-WangT #modelling #recognition

Bayesian Face Recognition Based on Gaussian Mixture Models (XW, XT), pp. 142–145.

ICPR-v4-2004-ZhouWG #estimation #using

Tracking Periodic Motion using Bayesian Estimation (HZ, AMW, PRG), pp. 725–728.

KDD-2004-JaroszewiczS #network #using

Interestingness of frequent itemsets using Bayesian networks as background knowledge (SJ, DAS), pp. 178–186.

KDD-2004-Poole #approach

Estimating the size of the telephone universe: a Bayesian Mark-recapture approach (DP), pp. 659–664.

KDD-2004-SmithE #framework #network

A Bayesian network framework for reject inference (ATS, CE), pp. 286–295.

KDD-2004-WrightY #distributed #network #privacy #semistructured data

Privacy-preserving Bayesian network structure computation on distributed heterogeneous data (RNW, ZY), pp. 713–718.

SIGIR-2004-YuTY #framework #information management #parametricity

A nonparametric hierarchical bayesian framework for information filtering (KY, VT, SY), pp. 353–360.

SIGIR-2004-Zhang #adaptation #classification #using

Using bayesian priors to combine classifiers for adaptive filtering (YZ0), pp. 345–352.

DAC-2003-FineZ #functional #generative #network #testing #using #verification

Coverage directed test generation for functional verification using bayesian networks (SF, AZ), pp. 286–291.

ICDAR-2003-ChoiCK #generative #network #online

Generation of Handwritten Characters with Bayesian network based On-line Handwriting Recognizers (HIC, SJC, JHK), p. 995–?.

ICDAR-2003-ChoK #modelling #network #online #recognition

Bayesian Network Modeling of Hangul Characters for On-line Handwriting Recognition (SJC, JHK), pp. 207–211.

ICEIS-v2-2003-BendouM #learning #network #semistructured data

Learning Bayesian Networks From Noisy Data (MB, PM), pp. 26–33.

ICEIS-v2-2003-CamposGM #abduction #network #probability #using

Partial Abductive Inference in Bayesian Networks By Using Probability Trees (LMdC, JAG, SM), pp. 83–91.

ICEIS-v2-2003-ColaceSFV #learning #network #ontology

Ontology Learning Through Bayesian Networks (FC, MDS, PF, MV), pp. 430–433.

ICEIS-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.

ICML-2003-CerquidesM #learning #modelling #naive bayes

Tractable Bayesian Learning of Tree Augmented Naive Bayes Models (JC, RLdM), pp. 75–82.

ICML-2003-Duff03a #approximate #markov

Diffusion Approximation for Bayesian Markov Chains (MOD), pp. 139–146.

ICML-2003-MooreW #learning #network

Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning (AWM, WKW), pp. 552–559.

ICML-2003-WongMCW #detection #network

Bayesian Network Anomaly Pattern Detection for Disease Outbreaks (WKW, AWM, GFC, MMW), pp. 808–815.

ICML-2003-ZhangXC #adaptation #learning

Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning (YZ, WX, JPC), pp. 896–903.

KDD-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.

MLDM-2003-DeventerDNK #modelling #testing #using

Using Test Plans for Bayesian Modeling (RD, JD, HN, OK), pp. 307–316.

MLDM-2003-KimJ #network #using

Using Bayesian Networks to Analyze Medical Data (ICK, YGJ), pp. 317–327.

SEKE-2003-Morasca #approach #evaluation #testing

A Bayesian Approach to Software Testing Evaluation (SM), pp. 706–713.

SIGIR-2003-ZaragozaHT #ad hoc #information retrieval

Bayesian extension to the language model for ad hoc information retrieval (HZ, DH, MET), pp. 4–9.

SAC-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.

ECIR-2002-CamposFH #documentation #network #retrieval

A Layered Bayesian Network Model for Document Retrieval (LMdC, JMFL, JFH), pp. 169–182.

ECIR-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.

ICML-2002-DashC #classification #naive bayes

Exact model averaging with naive Bayesian classifiers (DD, GFC), pp. 91–98.

ICML-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.

ICML-2002-ZhangL #bound #network

Representational Upper Bounds of Bayesian Networks (HZ, CXL), pp. 674–681.

ICPR-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.

ICPR-v1-2002-LezorayC #image

Bayesian Marker Extraction for Color Watershed in Segmenting Microscopic Images (OL, HC), pp. 739–742.

ICPR-v1-2002-Mignotte #multi #parametricity

Bayesian Rendering with Non-Parametric Multiscale Prior Model (MM), pp. 247–251.

ICPR-v1-2002-Souafi-BensafiPLE #classification #documentation #network

Bayesian Networks Classifiers Applied to Documents (SSB, MP, FL, HE), p. 483–?.

ICPR-v2-2002-GargPH #classification #network

Bayesian Networks as Ensemble of Classifiers (AG, VP, TSH), pp. 779–784.

ICPR-v2-2002-SkaffAC

Active Bayesian Color Constancy with Non-Uniform Sensors (SS, TA, JJC), pp. 681–684.

ICPR-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.

ICPR-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–?.

ICPR-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.

ICPR-v3-2002-QianCZ #approach #estimation #independence #multi

A Bayesian Approach to Simultaneous Motion Estimation of Multiple Independently Moving Objects (GQ, RC, QZ), p. 309–?.

ICPR-v3-2002-QiP #classification

Context-Sensitive Bayesian Classifiers and Application to Mouse Pressure Pattern Classification (Y(Q, RWP), pp. 448–451.

ICPR-v3-2002-SilvestreL #bound #classification #optimisation

Optimization of Neural Classifiers Based on Bayesian Decision Boundaries and Idle Neurons Pruning (MRS, LLL), pp. 387–390.

ICPR-v4-2002-MathisB #approach #classification #using

Classification Using a Hierarchical Bayesian Approach (CM, TMB), p. 103–?.

ICPR-v4-2002-StephensonMB #automation #network #recognition #speech

Mixed Bayesian Networks with Auxiliary Variables for Automatic Speech Recognition (TAS, MMD, HB), p. 293–?.

KDD-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.

KDD-2002-RidgewayM #analysis #dataset

Bayesian analysis of massive datasets via particle filters (GR, DM), pp. 5–13.

KR-2002-BeygelzimerR #complexity #learning #network

Inference Complexity as a Model-Selection Criterion for Learning Bayesian Networks (AB, IR), pp. 558–567.

SIGIR-2002-ChaiCN #classification #online

Bayesian online classifiers for text classification and filtering (KMAC, HLC, HTN), pp. 97–104.

SAC-2002-ElishRF #collaboration #learning #network

Evaluating collaborative software in supporting organizational learning with Bayesian Networks (MOE, DCR, JEF), pp. 992–996.

DAC-2001-BhanjaR #dependence #modelling #network #probability #process #using

Dependency Preserving Probabilistic Modeling of Switching Activity using Bayesian Networks (SB, NR), pp. 209–214.

ICDAR-2001-ChoK #modelling #network #online #recognition

Bayesian Network Modeling of Strokes and their Relationships for On-line Handwriting Recognition (SJC, JHK), pp. 86–90.

ICDAR-2001-HasegawaAM #algorithm #online #recognition

A Bayesian Bi-gram Scheme for HMM Online Handwriting Recognition Algorithm (TH, KA, TM), pp. 1012–1016.

ICSM-2001-Chulani #analysis #modelling #quality

Bayesian Analysis of Software Cost and Quality Models (SC), p. 565–?.

ICEIS-v1-2001-GamboaF #approach #design

Designing Intelligent Tutoring Systems: A Bayesian Approach (HG, ALNF), pp. 452–458.

ICML-2001-ChuKO #framework

A Unified Loss Function in Bayesian Framework for Support Vector Regression (WC, SSK, CJO), pp. 51–58.

ICML-2001-GartnerF #classification #named

WBCsvm: Weighted Bayesian Classification based on Support Vector Machines (TG, PAF), pp. 154–161.

ICML-2001-HamerlyE #predict

Bayesian approaches to failure prediction for disk drives (GH, CE), pp. 202–209.

ICML-2001-NgJ #classification #convergence #feature model

Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection (AYN, MIJ), pp. 377–384.

ICML-2001-ZadroznyE #classification #naive bayes #probability

Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers (BZ, CE), pp. 609–616.

MLDM-2001-PhamWS #classification #detection #network

Face Detection by Aggregated Bayesian Network Classifiers (TVP, MW, AWMS), pp. 249–262.

SEKE-2001-VincenziNMDR #guidelines

Bayesian-Learning Based Guidelines to determine Equivalente Mutants (AMRV, EYN, JCM, MED, RAFR), pp. 180–187.

SAT-2001-HorvitzRGKSC #approach #problem

A Bayesian Approach to Tackling Hard Computational Problems (Preliminary Report) (EH, YR, CPG, HAK, BS, DMC), pp. 376–391.

ICEIS-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.

ICEIS-2000-MilhoF #development #network

A User-Friendly Development Tool for Medical Diagnosis Based on Bayesian Networks (IM, ALNF), pp. 176–180.

ICML-2000-Domingos #classification #problem

Bayesian Averaging of Classifiers and the Overfitting Problem (PMD), pp. 223–230.

ICML-2000-HsuHW #classification #naive bayes #why

Why Discretization Works for Naive Bayesian Classifiers (CNH, HJH, TTW), pp. 399–406.

ICML-2000-KimN #learning #network #set

Learning Bayesian Networks for Diverse and Varying numbers of Evidence Sets (ZWK, RN), pp. 479–486.

ICML-2000-LiB #approach #clustering #markov #modelling #using

A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models (CL, GB), pp. 543–550.

ICML-2000-Strens #framework #learning

A Bayesian Framework for Reinforcement Learning (MJAS), pp. 943–950.

ICPR-v1-2000-HongengBN #framework #video

Bayesian Framework for Video Surveillance Application (SH, FB, RN), pp. 1164–1170.

ICPR-v1-2000-PalettaPP #analysis #learning #recognition #using

Learning Temporal Context in Active Object Recognition Using Bayesian Analysis (LP, MP, AP), pp. 1695–1699.

ICPR-v1-2000-PiaterG #learning #network #recognition

Feature Learning for Recognition with Bayesian Networks (JHP, RAG), pp. 1017–1020.

ICPR-v2-2000-LingC #bound #feature model #performance

Fast and Efficient Feature Extraction Based on Bayesian Decision Boundaries (LLL, HMC), pp. 2390–2393.

ICPR-v2-2000-RitterG #approach #identification #pattern matching #pattern recognition #recognition

A Bayesian Approach to Object Identification in Pattern Recognition (GR, MTG), pp. 2418–2421.

ICPR-v2-2000-VailayaJ #classification

Reject Option for VQ-Based Bayesian Classification (AV, AKJ), pp. 2048–2051.

ICPR-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.

ICPR-v3-2000-DemireklerKC #using

Fusing Length and Voicing Information, and HMM Decision Using a Bayesian Causal Tree against Insufficient Training Data (MD, FK, TÇ), pp. 3106–3109.

ICPR-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.

ICPR-v3-2000-MostafaPF #classification

A Two-Step Fuzzy-Bayesian Classification for High Dimensional Data (MGHM, TCP, AAF), pp. 3421–3424.

KDD-2000-KontkanenLMT #visualisation

Unsupervised Bayesian visualization of high-dimensional data (PK, JL, PM, HT), pp. 325–329.

KDD-2000-Tresp

The generalized Bayesian committee machine (VT), pp. 130–139.

KR-2000-Yelland #logic #network

An Alternative Combination of Bayesian Networks and Description Logics (PMY), pp. 225–234.

SIGIR-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.

CL-2000-Poole #information management #logic #representation

Logic, Knowledge Representation, and Bayesian Decision Theory (DP), pp. 70–86.

ICEIS-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.

ICML-1999-LangleyS #analysis #classification #naive bayes

Tractable Average-Case Analysis of Naive Bayesian Classifiers (PL, SS), pp. 220–228.

ICML-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.

KDD-1999-Cerquides #induction

Applying General Bayesian Techniques to Improve TAN Induction (JC), pp. 292–296.

KDD-1999-DaviesM #dataset #network

Bayesian Networks for Lossless Dataset Compression (SD, AWM), pp. 387–391.

ICML-1998-CristianiniSS #classification #scalability

Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space (NC, JST, PS), pp. 109–117.

ICML-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.

ICML-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.

ICML-1998-RamachandranM #network #refinement

Theory Refinement of Bayesian Networks with Hidden Variables (SR, RJM), pp. 454–462.

ICPR-1998-BamfordL #analysis #segmentation

Bayesian analysis of cell nucleus segmentation by a Viterbi search based active contour (PB, BCL), pp. 133–135.

ICPR-1998-CallariSF #approach #estimation #nondeterminism

Uncertainty in pose estimation: a Bayesian approach (FGC, GS, FPF), pp. 972–976.

ICPR-1998-KrebsW #3d #automation #generative #recognition

Automatic generation of Bayesian nets for 3D object recognition (BK, FMW), pp. 126–128.

ICPR-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.

KDD-1998-KontkanenMST #classification #feature model #named

BAYDA: Software for Bayesian Classification and Feature Selection (PK, PM, TS, HT), pp. 254–258.

KR-1998-Jaeger #infinity #network #random #reasoning #relational

Reasoning About Infinite Random Structures with Relational Bayesian Networks (MJ), pp. 570–581.

LICS-1998-Jaeger #convergence #network #relational

Convergence Results for Relational Bayesian Networks (MJ), pp. 44–55.

ICSM-1997-ZivR #maintenance #modelling #nondeterminism #testing

Constructing Bayesian-network models of software testing and maintenance uncertainties (HZ, DJR), p. 100–?.

ICML-1997-SuematsuHL #approach #learning #markov

A Bayesian Approach to Model Learning in Non-Markovian Environments (NS, AH, SL), pp. 349–357.

KDD-1997-Domingos #why

Why Does Bagging Work? A Bayesian Account and its Implications (PMD), pp. 155–158.

KDD-1997-TurmonMP #identification

Bayesian Inference for Identifying Solar Active Regions (MJT, SM, JP), pp. 267–270.

AKDDM-1996-CheesemanS #classification

Bayesian Classification (AutoClass): Theory and Results (PC, JS), pp. 153–180.

AKDDM-1996-Heckerman #information management #network

Bayesian Networks for Knowledge Discovery (DH), pp. 273–305.

ICML-1996-DomingosP #classification #independence

Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier (PMD, MJP), pp. 105–112.

ICML-1996-EzawaSN #learning #network #risk management

Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management (KJE, MS, SWN), pp. 139–147.

ICML-1996-FriedmanG #learning #network

Discretizing Continuous Attributes While Learning Bayesian Networks (NF, MG), pp. 157–165.

ICML-1996-SinghP #classification #learning #network #performance

Efficient Learning of Selective Bayesian Network Classifiers (MS, GMP), pp. 453–461.

ICML-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.

ICPR-1996-Brailovsky #approach #detection #probability

An approach to outlier detection based on Bayesian probabilistic model (VLB), pp. 70–74.

ICPR-1996-CoxMOY #feedback #image #named #retrieval

PicHunter: Bayesian relevance feedback for image retrieval (IJC, MLM, SMO, PNY), pp. 361–369.

ICPR-1996-DiasBD #classification #identification

Results of the use of Bayesian classifiers for identification of breast cancer cell nuclei (ÂVD, FB, MRD), pp. 508–512.

ICPR-1996-Gimelfarb96a #modelling #segmentation #simulation

Gibbs models for Bayesian simulation and segmentation of piecewise-uniform textures (GLG), pp. 760–764.

ICPR-1996-MoghaddamNP #image #similarity

A Bayesian similarity measure for direct image matching (BM, CN, AP), pp. 350–358.

ICPR-1996-PrantlGP #multi #network #using

Active fusion using Bayesian networks applied to multi-temporal remote sensing imagery (MP, HG, AP), pp. 890–894.

ICPR-1996-UtschickN #adaptation #network

Bayesian adaptation of hidden layers in Boolean feedforward neural networks (WU, JAN), pp. 229–233.

ICPR-1996-YimBA #segmentation #using

Bayesian range segmentation using focus cues (CY, ACB, JKA), pp. 482–486.

KDD-1996-Sahami #classification #dependence #learning

Learning Limited Dependence Bayesian Classifiers (MS), pp. 335–338.

CIKM-1995-ParkHC #automation #network #using

Automatic Thesaurus Construction Using Bayesian Networks (YCP, YSH, KSC), pp. 212–217.

ICML-1995-Cussens #algorithm #analysis #finite #learning

A Bayesian Analysis of Algorithms for Learning Finite Functions (JC), pp. 142–149.

ICML-1995-Heckerman #learning #network

Learning With Bayesian Networks (Abstract) (DH), p. 588.

ICML-1995-SinghP #algorithm #classification #comparison #induction

A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers (MS, GMP), pp. 497–505.

KDD-1995-EzawaN #information management #modelling #network #using

Knowledge Discovery in Telecommunication Services Data Using Bayesian Network Models (KJE, SWN), pp. 100–105.

KDD-1995-Pazzani #approach #classification

An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers (MJP), pp. 228–233.

KDD-1995-SpirtesM #learning #network

Learning Bayesian Networks with Discrete Variables from Data (PS, CM), pp. 294–299.

KDD-1995-Thiesson #network #quantifier #semistructured data

Accelerated Quantification of Bayesian Networks with Incomplete Data (BT), pp. 306–311.

SAC-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.

ICML-1994-Muggleton #induction #logic programming

Bayesian Inductive Logic Programming (SM), pp. 371–379.

KDD-1994-HeckermanGC #learning #network #statistics

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data (DH, DG, DMC), pp. 85–96.

CIKM-1993-EickJ #algorithm #classification #learning #search-based

Learning Bayesian Classification Rules through Genetic Algorithms (CFE, DJ), pp. 305–313.

ICML-1993-Connolly #clustering #concept #network

Constructing Hidden Variables in Bayesian Networks via Conceptual Clustering (DC), pp. 65–72.

ICML-1993-Schwalb #compilation #network

Compiling Bayesian Networks into Neural Networks (ES), pp. 291–297.

SIGIR-1993-TzerasH #automation #network

Automatic Indexing Based on Bayesian Inference Networks (KT, SH), pp. 22–34.

KDD-1991-WuSO #classification #heuristic #integration

Integration of Heuristic and Bayesian Approaches in a Pattern-Classification System (QW, PS, AO), pp. 249–260.

CHI-1989-Cole #comprehension #reasoning #visual notation

Understanding Bayesian reasoning via graphical displays (WGC), pp. 381–386.

ML-1988-CheesemanKSSTF #classification #named

AutoClass: A Bayesian Classification System (PC, JK, MS, JS, WT, DF), pp. 54–64.

SIGIR-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.

ICSE-1976-Chandy #design #modelling

Bayesian Models of Design Based on Intuition (KMC), pp. 281–285.