Stem bay$ (all stems)

92 papers:

ICML-2015-YenLZRD #approach #modelling #process

A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models (IEHY, XL, KZ, PKR, ISD), pp. 2418–2426.

KDD-2015-SatoN #online #probability

Stochastic Divergence Minimization for Online Collapsed Variational Bayes Zero Inference of Latent Dirichlet Allocation (IS, HN), pp. 1035–1044.

SAC-2015-Valverde-Rebaza #modelling #naive bayes #network #online #predict #social

A naïve Bayes model based on ovelapping groups for link prediction in online social networks (JCVR, AV, LB, TdPF, AdAL), pp. 1136–1141.

SIGMOD-2014-ZhangCPSX #named #network

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

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-c2-2014-HoangLJK #learning #process

Nonmyopic ϵ-Bayes-Optimal Active Learning of Gaussian Processes (TNH, BKHL, PJ, MSK), pp. 739–747.

ICML-c2-2014-KingmaW #performance

Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets (DPK, MW), pp. 1782–1790.

ICML-c2-2014-TitsiasL #probability

Doubly Stochastic Variational Bayes for non-Conjugate Inference (MKT, MLG), pp. 1971–1979.

ICPR-2014-FornoniC #learning #naive bayes #recognition

Scene Recognition with Naive Bayes Non-linear Learning (MF, BC), pp. 3404–3409.

KDD-2014-YangH #learning #parametricity

Learning with dual heterogeneity: a nonparametric bayes model (HY, JH), pp. 582–590.

ICML-c3-2013-BroderickKJ #named

MAD-Bayes: MAP-based Asymptotic Derivations from Bayes (TB, BK, MIJ), pp. 226–234.

ICML-c3-2013-HonorioJ #bound #exponential #fault

Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy (JH, TSJ), pp. 459–467.

KDD-2013-HarpazDLS #empirical

Empirical bayes model to combine signals of adverse drug reactions (RH, WD, PL, NHS), pp. 1339–1347.

KDIR-KMIS-2013-CheetiSC #adaptation #approach #classification #naive bayes #sentiment #syntax #using

Cross-domain Sentiment Classification using an Adapted Naïve Bayes Approach and Features Derived from Syntax Trees (SC, AS, DC), pp. 169–176.

RecSys-2013-KoenigsteinP #embedded #feature model #matrix #recommendation

Xbox movies recommendations: variational bayes matrix factorization with embedded feature selection (NK, UP), pp. 129–136.

CIKM-2012-ChenW #automation #classification #naive bayes

Automated feature weighting in naive bayes for high-dimensional data classification (LC, SW), pp. 1243–1252.

ICML-2012-SatoN

Rethinking Collapsed Variational Bayes Inference for LDA (IS, HN), p. 101.

KDD-2012-SatoKN #process

Practical collapsed variational bayes inference for hierarchical dirichlet process (IS, KK, HN), pp. 105–113.

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.

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-GermainLLMS #approach #kernel

A PAC-Bayes Sample-compression Approach to Kernel Methods (PG, AL, FL, MM, SS), pp. 297–304.

ICML-2011-Potetz #linear #problem #using

Estimating the Bayes Point Using Linear Knapsack Problems (BP), pp. 257–264.

ICML-2011-RoyLM #bound #polynomial #source code

From PAC-Bayes Bounds to Quadratic Programs for Majority Votes (JFR, FL, MM), pp. 649–656.

ICML-2011-SuSM #classification #multi #naive bayes #scalability #using

Large Scale Text Classification using Semisupervised Multinomial Naive Bayes (JS, JSS, SM), pp. 97–104.

MLDM-2011-LiuM #multi #naive bayes

Smoothing Multinomial Naïve Bayes in the Presence of Imbalance (AL, CEM), pp. 46–59.

DRR-2010-KimLT #classification #naive bayes #online

Naïve Bayes and SVM classifiers for classifying databank accession number sentences from online biomedical articles (JK, DXL, GRT), pp. 1–10.

ICALP-v2-2010-McIverMM #composition #probability

Compositional Closure for Bayes Risk in Probabilistic Noninterference (AM, LM, CM), pp. 223–235.

CIKM-2010-SonPS #classification #estimation #learning #naive bayes

Learning naïve bayes transfer classifier throughclass-wise test distribution estimation (JWS, SBP, HJS), pp. 1729–1732.

ICML-2010-DembczynskiCH #classification #multi #probability

Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains (KD, WC, EH), pp. 279–286.

ICPR-2010-BruneauGP #parametricity #probability

Aggregation of Probabilistic PCA Mixtures with a Variational-Bayes Technique Over Parameters (PB, MG, FP), pp. 702–705.

ICPR-2010-FuLTZ #classification #learning #music #naive bayes #retrieval

Learning Naive Bayes Classifiers for Music Classification and Retrieval (ZF, GL, KMT, DZ), pp. 4589–4592.

ICPR-2010-GodecLSB #naive bayes #online #random

On-Line Random Naive Bayes for Tracking (MG, CL, AS, HB), pp. 3545–3548.

LOPSTR-2010-SchumannCL #analysis #synthesis

Analysis of Air Traffic Track Data with the AutoBayes Synthesis System (JS, KC, AL), pp. 21–36.

ICDAR-2009-YinL #segmentation

A Variational Bayes Method for Handwritten Text Line Segmentation (FY, CLL), pp. 436–440.

ECIR-2009-TanCWX #adaptation #analysis #naive bayes #sentiment

Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis (ST, XC, YW, HX), pp. 337–349.

VLDB-2008-WangMGH #modelling #named #nondeterminism #probability #repository #scalability #visual notation

BayesStore: managing large, uncertain data repositories with probabilistic graphical models (DZW, EM, MNG, JMH), pp. 340–351.

ICML-2008-DoshiPR #learning #using

Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs (FD, JP, NR), pp. 256–263.

ICML-2008-Nijssen #classification

Bayes optimal classification for decision trees (SN), pp. 696–703.

ICPR-2008-BruneauGP #approach #modelling #reduction

Parameter-based reduction of Gaussian mixture models with a variational-Bayes approach (PB, MG, FP), pp. 1–4.

SIGIR-2008-HueteCFR #modelling #naive bayes #representation

Hierarchical naive bayes models for representing user profiles (JFH, LMdC, JMFL, MARM), pp. 711–712.

SAC-2008-DiamantiniP #detection

Borderline detection by Bayes vector quantizers (CD, DP), pp. 904–908.

HIMI-MTT-2007-ChoeLA #fuzzy #self

Self-help Troubleshooting by Q-KE-CLD Based on a Fuzzy Bayes Model (PC, MRL, JPA), pp. 391–400.

HIMI-MTT-2007-MarucciLC #approach #classification #fuzzy #using

Computer Classification of Injury Narratives Using a Fuzzy Bayes Approach: Improving the Model (HRM, MRL, HLC), pp. 500–506.

ECIR-2007-HeD #classification #naive bayes #using

Improving Naive Bayes Text Classifier Using Smoothing Methods (FH, XD), pp. 703–707.

SAC-2007-JinLSB #automation #categorisation #naive bayes #web

Automatic web pages categorization with ReliefF and Hidden Naive Bayes (XJ, RL, XS, RB), pp. 617–621.

CASE-2006-NishiM #composition #optimisation #petri net #problem

Decomposition of Petri Nets for Optimization of Routing Problem for AGVs in Semiconductor Fabrication Bays (TN, RM), pp. 236–241.

ECIR-2006-YinP #adaptation #classification #naive bayes #rank

Adapting the Naive Bayes Classifier to Rank Procedural Texts (LY, RP), pp. 179–190.

ICML-2006-Cesa-BianchiGZ #classification

Hierarchical classification: combining Bayes with SVM (NCB, CG, LZ), pp. 177–184.

ICML-2006-DenisMR #classification #learning #naive bayes #performance

Efficient learning of Naive Bayes classifiers under class-conditional classification noise (FD, CNM, LR), pp. 265–272.

ICPR-v3-2006-Martinez-ArroyoS #classification #learning #naive bayes

Learning an Optimal Naive Bayes Classifier (MMA, LES), pp. 1236–1239.

ICPR-v4-2006-Martinez-ArroyoS06a #classification #learning #naive bayes

Learning an Optimal Naive Bayes Classifier (MMA, LES), p. 958.

SEKE-2006-HaiderC #estimation #fault

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

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-LavioletteM #bound #classification

PAC-Bayes risk bounds for sample-compressed Gibbs classifiers (FL, MM), pp. 481–488.

ICML-2005-LowdD #estimation #modelling #naive bayes #probability

Naive Bayes models for probability estimation (DL, PMD), pp. 529–536.

ICML-2005-ZhangJS #naive bayes #ranking

Augmenting naive Bayes for ranking (HZ, LJ, JS), pp. 1020–1027.

KDD-2005-Kolcz #classification #naive bayes

Local sparsity control for naive Bayes with extreme misclassification costs (AK), pp. 128–137.

SAC-2005-HanXZG #ambiguity #naive bayes

A hierarchical naive Bayes mixture model for name disambiguation in author citations (HH, WX, HZ, CLG), pp. 1065–1069.

ICML-2004-GoldenbergM #learning #scalability

Tractable learning of large Bayes net structures from sparse data (AG, AWM).

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-v3-2004-SotocaSP #multi #naive bayes #set #using

Attribute Relevance in Multiclass Data Sets Using the Naive Bayes Rule (JMS, JSS, FP), pp. 426–429.

KDD-2004-PavlovBDKP #classification #clustering #documentation #multi #naive bayes #preprocessor

Document preprocessing for naive Bayes classification and clustering with mixture of multinomials (DP, RB, BD, SK, JP), pp. 829–834.

SAC-2004-AmorBE #detection #naive bayes

Naive Bayes vs decision trees in intrusion detection systems (NBA, SB, ZE), pp. 420–424.

ASE-2003-FischerS #analysis #image

Applying AutoBayes to the Analysis of Planetary Nebulae Images (BF, JS), pp. 337–342.

ECIR-2003-PengS #classification #modelling #n-gram #naive bayes

Combining Naive Bayes and n-Gram Language Models for Text Classification (FP, DS), pp. 335–350.

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

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

ICML-2003-EngelMM #approach #difference #learning #process

Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning (YE, SM, RM), pp. 154–161.

ICML-2003-RennieSTK #classification #naive bayes

Tackling the Poor Assumptions of Naive Bayes Text Classifiers (JDR, LS, JT, DRK), pp. 616–623.

ICML-2002-YangW #classification

Non-Disjoint Discretization for Naive-Bayes Classifiers (YY, GIW), pp. 666–673.

ICPR-v1-2002-CarvalhoSDR #constraints #estimation #linear

Bayes Information Criterion for Tikhonov Regularization with Linear Constraints: Application to Spectral Data Estimation (PDC, AS, AD, BR), pp. 696–700.

ICPR-v1-2002-SebeLCGH #classification #naive bayes #recognition #using

Emotion Recognition Using a Cauchy Naive Bayes Classifier (NS, MSL, IC, AG, TSH), p. 17–?.

ICPR-v2-2002-Keren #identification #naive bayes #using

Painter Identification Using Local Features and Naive Bayes (DK), pp. 474–477.

SIGIR-2002-KimRL #classification #estimation #multi #naive bayes #parametricity

A new method of parameter estimation for multinomial naive bayes text classifiers (SBK, HCR, HSL), pp. 391–392.

CADE-2002-WhalenSF #automation #certification #named #synthesis

AutoBayes/CC — Combining Program Synthesis with Automatic Code Certification — System Description (MWW, JS, BF), pp. 290–294.

ICDAR-2001-BahlmannB #fault #online #probability #recognition #similarity

Measuring HMM Similarity with the Bayes Probability of Error and its Application to Online Handwriting Recognition (CB, HB), pp. 406–411.

ICDAR-2001-ShiOWK #clustering #distance #pseudo #recognition

Clustering with Projection Distance and Pseudo Bayes Discriminant Function for Handwritten Numeral Recognition (MS, WO, TW, FK), pp. 1007–1011.

ICML-2001-ZhangL #naive bayes

Learnability of Augmented Naive Bayes in Nonimal Domains (HZ, CXL), pp. 617–623.

KDD-2001-DuMouchelP #empirical #multi

Empirical bayes screening for multi-item associations (WD, DP), pp. 67–76.

ICML-2000-Heskes #empirical #learning

Empirical Bayes for Learning to Learn (TH), pp. 367–374.

ICML-2000-RychetskySG

Direct Bayes Point Machines (MR, JST, MG), pp. 815–822.

SIGIR-2000-AslamM #probability

Bayes optimal metasearch: a probabilistic model for combining the results (JAA, MHM), pp. 379–381.

SIGIR-2000-KimHZ #classification #naive bayes

Text filtering by boosting naive bayes classifiers (YHK, SYH, BTZ), pp. 168–175.

ICDAR-1999-KangL #classification #fault

Combining Classifiers based on Minimization of a Bayes Error Rate (HJK, SWL), pp. 398–401.

ICML-1999-MladenicG #feature model #naive bayes

Feature Selection for Unbalanced Class Distribution and Naive Bayes (DM, MG), pp. 258–267.

KDD-1999-MeretakisW #classification #naive bayes #using

Extending Naïve Bayes Classifiers Using Long Itemsets (DM, BW), pp. 165–174.

KDD-1998-RidgewayMRO #classification #naive bayes

Interpretable Boosted Naïve Bayes Classification (GR, DM, TR, JO), pp. 101–104.

ICPR-1996-DoeringW #classification #composition #cost analysis #network #set

Feedforward neural networks for Bayes-optimal classification: investigations into the influence of the composition of the training set on the cost function (AD, HW), pp. 219–223.

ICPR-1996-SzeL #algorithm #bound #branch #classification

Branch and bound algorithm for the Bayes classifier (LS, CHL), pp. 705–709.

ICPR-1996-TumerG #classification #fault

Estimating the Bayes error rate through classifier combining (KT, JG), pp. 695–699.

KDD-1996-Kohavi #classification #hybrid #scalability

Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid (RK), pp. 202–207.

KDD-1995-Glymour #modelling #predict

Available Technology for Discovering Causal Models, Building Bayes Nets, and Selecting Predictors: The TETRAD II Program (CG), pp. 130–135.

ML-1989-Buntine #classification #learning #using

Learning Classification Rules Using Bayes (WLB), pp. 94–98.