14.5.11 Bayesian Learning, Bayes Network, Bayesian Networks

Chapter Contents (Back)
Bayes Nets. Bayesian Learning.
See also Bayesian Networks, Bayes Nets.
See also Bayesian Clustering, Bayes Classifier.
See also Bayesian Optimization.

Bernardo, J.M., and Smith, A.F.M.,
Bayesian Theory,
John Wileyand Sons, 2000. BibRef 0001

Jefferys, W.H., and Berger, J.O.,
Occam's Razor and Bayesian Analysis,
AmSci(80), 1992, pp. 64-72. BibRef 9200

Smith, A.F.M., and Spiegelhalter, D.J.,
Bayes factors and choice criteria for linear models,
RoyalStat(B-42), 1980, pp. 213-220. BibRef 8000

Belforte, G., Bona, B., and Tempo, R.,
Conditional Allocation and Stopping Rules in Bayesian Pattern Recognition,
PAMI(8), No. 4, July 1986, pp. 502-511. BibRef 8607

Stirling, W.C., and Swindlehurst, A.L.,
Decision-Directed Multivariate Empirical Bayes Classification with Nonstationary Priors,
PAMI(9), No. 5, September 1987, pp. 644-660. BibRef 8709

Lowe, D.G., and Webb, A.R.,
Optimized Feature Extraction and the Bayes Decision in Feed-Forward Classifier Networks,
PAMI(13), No. 4, April 1991, pp. 355-364.
IEEE DOI BibRef 9104

Domingos, P., and Pazzani, M.,
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss,
MachLearn(29), 1997, pp. 103-130. BibRef 9700

Friedman, N., Geiger, D., and Goldszmid, M.,
Bayesian Network Classifiers,
MachLearn(29), 1997, No. 2, pp. 131-163. BibRef 9700

Grenander, U., Srivastava, A., and Miller, M.I.,
Asymptotic performance analysis of Bayesian object recognition,
IT(46), No. 4, April 2000, pp. 1658-1666. BibRef 0004

Magni, P., Bellazzi, R., de Nicolao, G.,
Bayesian Function Learning Using MCMC Methods,
PAMI(20), No. 12, December 1998, pp. 1319-1331.
IEEE DOI BibRef 9812

Pillonetto, G.[Gianluigi], Dinuzzo, F.[Francesco], de Nicolao, G.[Giuseppe],
Bayesian Online Multitask Learning of Gaussian Processes,
PAMI(32), No. 2, February 2010, pp. 193-205.
IEEE DOI 1001
Bayesian learning. BibRef

Li, T.F.[Tze Fen],
Bayes empirical Bayes approach to unsupervised learning of parameters in pattern recognition,
PR(33), No. 2, February 2000, pp. 333-340.
Elsevier DOI 0001
BibRef

Li, T.F.[Tze Fen], Chang, S.C.[Shui-Ching],
Classification on defective items using unidentified samples,
PR(38), No. 1, January 2005, pp. 51-58.
Elsevier DOI 0410
BibRef

Guo, G.D.[Guo-Dong], Ma, S.D.[Song-De],
Bayesian learning, global competition and unsupervised image segmentation,
PRL(21), No. 2, February 2000, pp. 107-116. 0003
BibRef

Yuille, A.L.[Alan L.], Coughlan, J.M.[James M.],
Fundamental Limits of Bayesian Inference: Order Parameters and Phase Transitions for Road Tracking,
PAMI(22), No. 2, February 2000, pp. 160-173.
IEEE DOI 0003
Road Following. BibRef

Rangarajan, A., Coughlan, J.M., Yuille, A.L.,
A bayesian network framework for relational shape matching,
ICCV03(671-678).
IEEE DOI 0311
BibRef

Sarkar, S.[Sudeep], Chavali, S.[Srikanth],
Modeling Parameter Space Behavior of Vision Systems Using Bayesian Networks,
CVIU(79), No. 2, August 2000, pp. 185-223.
DOI Link 0008
BibRef

Paulus, D., Hornegger, J., Niemann, H.,
A Framework for Statistical 3-D Object Recognition,
PRL(18), No. 11-13, November 1997, pp. 1153-1157.
PS File. 9806
BibRef

Hornegger, J.[Joachim], Niemann, H.[Heinrich],
Probabilistic Modeling and Recognition of 3-D Objects,
IJCV(39), No. 3, September-October 2000, pp. 229-251.
DOI Link 0101
BibRef

Hornegger, J., Paulus, D., and Niemann, H.,
Probabilistic Modeling in Computer Vision,
HCVA99(Vol 2, 817-854).
PS File. BibRef 9900

Hornegger, J., Niemann, H.,
Statistical Learning, Localization, and Identification of Objects,
ICCV95(914-919).
IEEE DOI BibRef 9500

Hornegger, J., Niemann, H.,
A Bayesian Approach to Learn and Classify 3D Objects from Intensity Images,
ICPR94(B:557-559).
IEEE DOI BibRef 9400

Hornegger, J.[Joachim], Welker, V.[Volkmar], Niemann, H.[Heinrich],
Localization and classification based on projections,
PR(35), No. 6, June 2002, pp. 1225-1235.
Elsevier DOI 0203
BibRef

Nock, R.[Richard], Sebban, M.[Marc],
A Bayesian boosting theorem,
PRL(22), No. 3-4, March 2001, pp. 413-419.
Elsevier DOI 0105
BibRef

Piro, P.[Paolo], Nock, R.[Richard], Nielsen, F.[Frank], Barlaud, M.[Michel],
Boosting Bayesian MAP Classification,
ICPR10(661-665).
IEEE DOI 1008

See also Boosting k-NN for Categorization of Natural Scenes. BibRef

Nielsen, F.[Frank],
Generalized Bhattacharyya and Chernoff upper bounds on Bayes error using quasi-arithmetic means,
PRL(42), No. 1, 2014, pp. 25-34.
Elsevier DOI 1404
Affinity coefficient BibRef

Nock, R., Ali, W.B.H., d'Ambrosio, R., Nielsen, F., Barlaud, M.,
Gentle Nearest Neighbors Boosting over Proper Scoring Rules,
PAMI(37), No. 1, January 2015, pp. 80-93.
IEEE DOI 1412
Boosting BibRef

Peña, J.M.[Jose Manuel], Lozano, J.A.[Jose Antonio], Larrañaga, P.[Pedro], Inza, I.[Iñaki],
Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks,
PAMI(23), No. 6, June 2001, pp. 590-603.
IEEE DOI 0106
Unsupervised learning of conditional Gaussian networks, reject features that have low correlation with others. BibRef

Kupinski, M.A., Edwards, D.C., Giger, M.L., Metz, C.E.,
Ideal observer approximation using bayesian classification neural networks,
MedImg(20), No. 9, September 2001, pp. 886-899.
IEEE Top Reference. 0110

See also Ideal Observers and Optimal ROC Hypersurfaces in N-Class Classification. BibRef

Yin, H., Allinson, N.M.,
Bayesian self-organising map for Gaussian mixtures,
VISP(148), No. 4, August 2001, pp. 234-240. 0201
BibRef

Mitra, S.K.[Suman K.], Lee, T.W.[Te-Won], Goldbaum, M.[Michael],
A Bayesian network based sequential inference for diagnosis of diseases from retinal images,
PRL(26), No. 4, March 2005, pp. 459-470.
Elsevier DOI 0501
BibRef

Gurwicz, Y.[Yaniv], Lerner, B.[Boaz],
Bayesian network classification using spline-approximated kernel density estimation,
PRL(26), No. 11, August 2005, pp. 1761-1771.
Elsevier DOI 0506
BibRef
Earlier:
Rapid spline-based kernel density estimation for bayesian networks,
ICPR04(III: 700-703).
IEEE DOI 0409
BibRef

Gurwicz, Y.[Yaniv], Lerner, B.[Boaz],
Bayesian Class-Matched Multinet Classifier,
SSPR06(145-153).
Springer DOI 0608
BibRef

Yehezkel, R.[Raanan], Lerner, B.[Boaz],
Bayesian Network Structure Learning by Recursive Autonomy Identification,
SSPR06(154-162).
Springer DOI 0608
BibRef

Gurwicz, Y.[Yaniv], Yehezkel, R.[Raanan], Lachover, B.[Boaz],
Multiclass object classification for real-time video surveillance systems,
PRL(32), No. 6, 15 April 2011, pp. 805-815.
Elsevier DOI 1103
Feature selection; Object classification; Video surveillance BibRef

Webb, G.I., Boughton, J., and Wang, Z.,
Not So Naive Bayes: Aggregating One-Dependence Estimators,
MachLearn(58), 2005, No. 1, pp. 5-24. BibRef 0500

Kuncheva, L.I.[Ludmila I.],
On the optimality of Naïve Bayes with dependent binary features,
PRL(27), No. 7, May 2006, pp. 830-837.
Elsevier DOI 0604
Statistical pattern recognition; Naive Bayes classifier (NB); Optimality of NB; Dependent binary features BibRef

Liu, Q.H.[Qiu-Hua], Liao, X.J.[Xue-Jun], Carin, H.L.[Hui Li], Stack, J.R.[Jason R.], Carin, L.[Lawrence],
Semisupervised Multitask Learning,
PAMI(31), No. 6, June 2009, pp. 1074-1086.
IEEE DOI 0904
BibRef

Williams, D.[David], Liao, X.J.[Xue-Jun], Xue, Y.[Ya], Carin, L.[Lawrence], Krishnapuram, B.[Balaji],
On Classification with Incomplete Data,
PAMI(29), No. 3, March 2007, pp. 427-436.
IEEE DOI 0702
Feature vectors have missing features. Supervised regression algorithm. BibRef

Galan, S.F.,
Belief updating in Bayesian networks by using a criterion of minimum time,
PRL(29), No. 4, 1 March 2008, pp. 465-482.
Elsevier DOI 0711
Bayesian network; Variable elimination; Elimination ordering; Clustering algorithms; Triangulation; Criterion of minimum time BibRef

Kuncheva, L.I.[Ludmila I.], Hoare, Z.[Zoe],
Error-Dependency Relationships for the Naïve Bayes Classifier with Binary Features,
PAMI(30), No. 4, April 2008, pp. 735-740.
IEEE DOI 0803
BibRef

Zhao, K.G.[Kai-Guang], Popescu, S.[Sorin], Zhang, X.S.[Xue-Song],
Bayesian Learning with Gaussian Processes for Supervised Classification of Hyperspectral Data,
PhEngRS(74), No. 10, October 2008, pp. 1223-1234.
WWW Link. 0804
A novel Bayesian kernel learning machine known as Gaussian Processes introduced into the remote sensing community to classify hyperspectral data. BibRef

Marttinen, P.[Pekka], Tang, J.[Jing], de Baets, B.[Bernard], Dawyndt, P.[Peter], Corander, J.[Jukka],
Bayesian Clustering of Fuzzy Feature Vectors Using a Quasi-Likelihood Approach,
PAMI(31), No. 1, January 2009, pp. 74-85.
IEEE DOI 0812
BibRef

Langseth, H.[Helge], Nielsen, T.D.[Thomas D.],
Latent classification models for binary data,
PR(42), No. 11, November 2009, pp. 2724-2736.
Elsevier DOI 0907
Classification; Binary images; Bayesian networks; Variational inference BibRef

Hasanat, M.H.A.[Mozaherul Hoque Abul], Ramachandram, D.[Dhanesh], Mandava, R.[Rajeswari],
Bayesian belief network learning algorithms for modeling contextual relationships in natural imagery: a comparative study,
AIR(34), No. 4, December 2010, pp. 291-308.
WWW Link. 1208
BibRef

Abbasnejad, M.E.[M. Ehsan], Ramachandram, D.[Dhanesh], Mandava, R.[Rajeswari],
Optimizing Kernel Functions Using Transfer Learning from Unlabeled Data,
ICMV09(111-117).
IEEE DOI 0912
BibRef

Barrat, S.[Sabine], Tabbone, S.A.[Salvatore A.],
Modeling, Classifying and Annotating Weakly Annotated Images Using Bayesian Network,
JVCIR(21), No. 4, May 2010, pp. 355-363.
Elsevier DOI 1006
BibRef
Earlier: ICDAR09(1201-1205).
IEEE DOI 0907
BibRef
Earlier:
Classification and Automatic Annotation Extension of Images Using Bayesian Network,
SSPR08(937-946).
Springer DOI 0812
Probabilistic graphical models; Bayesian networks; Image classification; Image annotation; Semantic similarity; Wordnet; Visual features; Bayesian classifier BibRef

Bouzaieni, A.[Abdessalem], Tabbone, S.A.[Salvatore A.],
Image Annotation Using a Semantic Hierarchy,
SSSPR18(3-13).
Springer DOI 1810
BibRef
Earlier:
Images Annotation Extension Based on User Feedback,
ACIVS17(418-430).
Springer DOI 1712
BibRef

Bouzaieni, A.[Abdessalem], Barrat, S.[Sabine], Tabbone, S.A.[Salvatore A.],
Automatic annotation extension and classification of documents using a probabilistic graphical model,
ICDAR15(316-320)
IEEE DOI 1511
BibRef
Earlier: A1, A3, A2:
Automatic Images Annotation Extension Using a Probabilistic Graphical Model,
CAIP15(II:579-590).
Springer DOI 1511
BibRef

Eches, O., Dobigeon, N., Mailhes, C., Tourneret, J.Y.,
Bayesian Estimation of Linear Mixtures Using the Normal Compositional Model: Application to Hyperspectral Imagery,
IP(19), No. 6, June 2010, pp. 1403-1413.
IEEE DOI 1006
BibRef

Eches, O., Dobigeon, N., Tourneret, J.Y.,
Enhancing Hyperspectral Image Unmixing With Spatial Correlations,
GeoRS(49), No. 11, November 2011, pp. 4239-4247.
IEEE DOI 1112
BibRef

Uezato, T., Fauvel, M., Dobigeon, N.,
Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization,
GeoRS(56), No. 7, July 2018, pp. 4098-4108.
IEEE DOI 1807
geophysical image processing, geophysical signal processing, geophysical techniques, hyperspectral imaging, optical radar, spectral unmixing (SU) BibRef

Wong, T.T.[Tzu-Tsung], Chang, L.H.[Liang-Hao],
Individual attribute prior setting methods for naive Bayesian classifiers,
PR(44), No. 5, May 2011, pp. 1041-1047.
Elsevier DOI 1101
Dirichlet distribution; Generalized Dirichlet distribution; Naive Bayesian classifier; Prior distribution; Selective naive Bayes BibRef

Jamil, T.[Tahira], ter Braak, C.J.F.[Cajo J.F.],
Selection properties of type II maximum likelihood (empirical Bayes) in linear models with individual variance components for predictors,
PRL(33), No. 9, 1 July 2012, pp. 1205-1212.
Elsevier DOI 1202
Automatic relevance detection; Empirical Bayes; LASSO; Sparse model; Type II maximum likelihood; Relevance vector machine BibRef

Thomas, A., Oommen, B.J.[B. John],
The fundamental theory of optimal 'Anti-Bayesian' parametric pattern classification using order statistics criteria,
PR(46), No. 1, January 2013, pp. 376-388.
Elsevier DOI 1209
BibRef
And:
Optimal 'anti-bayesian' Parametric Pattern Classification Using Order Statistics Criteria,
CIARP12(1-13).
Springer DOI 1209
BibRef
Earlier:
Optimal 'Anti-Bayesian' Parametric Pattern Classification for the Exponential Family Using Order Statistics Criteria,
ICIAR12(I: 11-18).
Springer DOI 1206
Pattern classification; Order statistics; Reduction of training patterns; Prototype reduction schemes; Classification by moments of order statistics BibRef

Oommen, B.J.[B. John], Thomas, A.,
'Anti-Bayesian' parametric pattern classification using order statistics criteria for some members of the exponential family,
PR(47), No. 1, 2014, pp. 40-55.
Elsevier DOI 1310
Pattern classification BibRef

Thomas, A.[Anu], Oommen, B.J.[B. John],
On Achieving Near-Optimal 'Anti-Bayesian' Order Statistics-Based Classification for Asymmetric Exponential Distributions,
CAIP13(368-376).
Springer DOI 1308
BibRef

Thomas, A.[Anu], Oommen, B.J.[B. John],
Corrigendum to three papers that deal with 'Anti'-Bayesian Pattern Recognition [Pattern Recognition],
PR(47), No. 6, 2014, pp. 2301-2302.
Elsevier DOI 1403
BibRef

Thomas, A.[Anu], Oommen, B.J.[B. John],
Order statistics-based parametric classification for multi-dimensional distributions,
PR(46), No. 12, 2013, pp. 3472-3482.
Elsevier DOI 1308
Classification using Order Statistics (OS) BibRef

Hammer, H.L.[Hugo Lewi], Yazidi, A.[Anis], Oommen, B.J.[B. John],
On the classification of dynamical data streams using novel 'Anti-Bayesian' techniques,
PR(76), No. 1, 2018, pp. 108-124.
Elsevier DOI 1801
Anti-Bayesian classification BibRef

Zeng, J.[Jia], Cheung, W.K.[William K.], Liu, J.M.[Ji-Ming],
Learning Topic Models by Belief Propagation,
PAMI(35), No. 5, May 2013, pp. 1121-1134.
IEEE DOI 1304
Latent Dirichlet allocation-hierarchical Bayesian model. BibRef

Mello, M.P.[Marcio Pupin], Risso, J.[Joel], Atzberger, C.[Clement], Aplin, P.[Paul], Pebesma, E.[Edzer], Oliveira Vieira, C.A.[Carlos Antonio], Theodor Rudorff, B.F.[Bernardo Friedrich],
Bayesian Networks for Raster Data (BayNeRD): Plausible Reasoning from Observations,
RS(5), No. 11, 2013, pp. 5999-6025.
DOI Link 1312
BibRef

Ko, S.[Song], Kim, D.W.[Dae-Won],
An efficient node ordering method using the conditional frequency for the K2 algorithm,
PRL(40), No. 1, 2014, pp. 80-87.
Elsevier DOI 1403
Bayesian networks BibRef

Carvalho, A.M.[Alexandra M.], Adão, P.[Pedro], Mateus, P.[Paulo],
Hybrid learning of Bayesian multinets for binary classification,
PR(47), No. 10, 2014, pp. 3438-3450.
Elsevier DOI 1406
Conditional log-likelihood BibRef

Liu, Y., Simeone, O., Haimovich, A.M., Su, W.,
Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network,
SPLetters(21), No. 9, Sept 2014, pp. 1135-1139.
IEEE DOI 1406
Bayes methods BibRef

Filippone, M., Girolami, M.,
Pseudo-Marginal Bayesian Inference for Gaussian Processes,
PAMI(36), No. 11, November 2014, pp. 2214-2226.
IEEE DOI 1410
Approximation methods BibRef

Filippone, M.[Maurizio],
Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMC,
ICPR14(614-619)
IEEE DOI 1412
Annealing BibRef

Ortega, P.A.[Pedro A.],
Subjectivity, Bayesianism, and causality,
PRL(64), No. 1, 2015, pp. 63-70.
Elsevier DOI 1509
Subjectivity BibRef

Duan, H.P.[Hui-Ping], Zhang, L.[Lizao], Fang, J.[Jun], Huang, L.[Lei], Li, H.B.[Hong-Bin],
Pattern-Coupled Sparse Bayesian Learning for Inverse Synthetic Aperture Radar Imaging,
SPLetters(22), No. 11, November 2015, pp. 1995-1999.
IEEE DOI 1509
Gaussian processes BibRef

Fang, J.[Jun], Zhang, L.[Lizao], Li, H.B.[Hong-Bin],
Two-Dimensional Pattern-Coupled Sparse Bayesian Learning via Generalized Approximate Message Passing,
IP(25), No. 6, June 2016, pp. 2920-2930.
IEEE DOI 1605
Bayes methods BibRef

Bouguelia, M.R.[Mohamed-Rafik], Belaïd, Y.[Yolande], Belaïd, A.[Abdel],
An adaptive streaming active learning strategy based on instance weighting,
PRL(70), No. 1, 2016, pp. 38-44.
Elsevier DOI 1602
BibRef
Earlier:
Efficient Active Novel Class Detection for Data Stream Classification,
ICPR14(2826-2831)
IEEE DOI 1412
BibRef
Earlier:
A Stream-Based Semi-supervised Active Learning Approach for Document Classification,
ICDAR13(611-615)
IEEE DOI 1312
Classification. Accuracy. graph theory BibRef

Philippot, E.[Emilie], Belaid, Y.[Yolande], Belaid, A.[Abdel],
Learning algorithms of form structure for Bayesian networks,
ICIP10(2149-2152).
IEEE DOI 1009
BibRef
And:
Bayesian Networks Learning Algorithms for Online Form Classification,
ICPR10(1981-1984).
IEEE DOI 1008
BibRef

Gan, H.X.[Hong-Xiao], Zhang, Y.[Yang], Song, Q.[Qun],
Bayesian belief network for positive unlabeled learning with uncertainty,
PRL(90), No. 1, 2017, pp. 28-35.
Elsevier DOI 1704
PU learning BibRef

Duan, H., Yang, L., Fang, J., Li, H.,
Fast Inverse-Free Sparse Bayesian Learning via Relaxed Evidence Lower Bound Maximization,
SPLetters(24), No. 6, June 2017, pp. 774-778.
IEEE DOI 1705
Bayes methods, Compressed sensing, Computational complexity, Covariance matrices, Matching pursuit algorithms, Signal processing algorithms, Sparse matrices, Compressed sensing, inverse-free sparse Bayesian learning (SBL), relaxed, evidence, lower, bound, (relaxed-ELBO) BibRef

Servajean, M., Joly, A., Shasha, D., Champ, J., Pacitti, E.,
Crowdsourcing Thousands of Specialized Labels: A Bayesian Active Training Approach,
MultMed(19), No. 6, June 2017, pp. 1376-1391.
IEEE DOI 1705
Bayes methods, Crowdsourcing, Labeling, Multimedia communication, Prediction algorithms, Probability distribution, Training, Bayes methods, Crowdsourcing, Taylor series, parameter, estimation BibRef

Amirkhani, H.[Hossein], Rahmati, M.[Mohammad], Lucas, P.J.F.[Peter J.F.], Hommersom, A.[Arjen],
Exploiting Experts: Knowledge for Structure Learning of Bayesian Networks,
PAMI(39), No. 11, November 2017, pp. 2154-2170.
IEEE DOI 1710
Bayes methods, Computational modeling, Data models, Markov processes, Random variables, Reliability, experts' accuracy, experts' knowledge, marginalization-based score, BibRef

Nautsch, A., Meuwly, D., Ramos, D., Lindh, J., Busch, C.,
Making Likelihood Ratios Digestible for Cross-Application Performance Assessment,
SPLetters(24), No. 10, October 2017, pp. 1552-1556.
IEEE DOI 1710
Bayes methods, biometrics (access control), BibRef

Chen, W.,
Simultaneous Sparse Bayesian Learning With Partially Shared Supports,
SPLetters(24), No. 11, November 2017, pp. 1641-1645.
IEEE DOI 1710
Bayes methods, Estimation, Parametric statistics, Sparse representation, Bayesian information criterion (BIC), sparse Bayesian learning, sparse, estimation BibRef

Nie, S., Zheng, M., Ji, Q.,
The Deep Regression Bayesian Network and Its Applications: Probabilistic Deep Learning for Computer Vision,
SPMag(35), No. 1, January 2018, pp. 101-111.
IEEE DOI 1801
Computational modeling, Data models, Machine learning, Probabilistic logic, Training data, Uncertainty BibRef

Meng, X., Wu, S., Zhu, J.,
A Unified Bayesian Inference Framework for Generalized Linear Models,
SPLetters(25), No. 3, March 2018, pp. 398-402.
IEEE DOI 1802
Approximation algorithms, Bayes methods, Compressed sensing, Inference algorithms, Message passing, Sea measurements, vector approximate message passing (VAMP) BibRef

Wang, Y.H.[Yong-Heng], Gao, H.[Hui], Chen, G.[Guidan],
Predictive complex event processing based on evolving Bayesian networks,
PRL(105), 2018, pp. 207-216.
Elsevier DOI 1804
Event stream, Predictive complex event processing, Evolving Bayesian networks BibRef

Šošic, A.[Adrian], Zoubir, A.M.[Abdelhak M.], Koeppl, H.[Heinz],
A Bayesian Approach to Policy Recognition and State Representation Learning,
PAMI(40), No. 6, June 2018, pp. 1295-1308.
IEEE DOI 1805
Bayes methods, Behavioral sciences, Computational modeling, Data models, Learning systems, Markov processes, policy recognition BibRef

Nguyen, T.H., Simsekli, U., Richard, G., Cemgil, A.T.,
Efficient Bayesian Model Selection in PARAFAC via Stochastic Thermodynamic Integration,
SPLetters(25), No. 5, May 2018, pp. 725-729.
IEEE DOI 1805
Approximation algorithms, Bayes methods, Computational modeling, Mathematical model, Signal processing algorithms, tensor factorization BibRef

Benjumeda, M.[Marco], Luengo-Sanchez, S.[Sergio], Larrañaga, P.[Pedro], Bielza, C.[Concha],
Tractable learning of Bayesian networks from partially observed data,
PR(91), 2019, pp. 190-199.
Elsevier DOI 1904
Structural expectation-maximization, Bayesian network, Incomplete data, Inference complexity, Structure learning BibRef

Abdulkareem, S.A.[Shaheen A.], Mustafa, Y.T.[Yaseen T.], Augustijn, E.W.[Ellen-Wien], Filatova, T.[Tatiana],
Bayesian networks for spatial learning: a workflow on using limited survey data for intelligent learning in spatial agent-based models,
GeoInfo(23), No. 2, Apriul 2019, pp. 243-268.
Springer DOI 1906
BibRef

Liang, J., Ahmad, B.I., Gan, R., Langdon, P., Hardy, R., Godsill, S.,
On Destination Prediction Based on Markov Bridging Distributions,
SPLetters(26), No. 11, November 2019, pp. 1663-1667.
IEEE DOI 1911
Prediction algorithms, Mathematical model, Markov processes, Signal processing algorithms, Bayes methods, Kalman filter BibRef

Zhou, Y.[Yang], Cheung, Y.M.[Yiu-Ming],
Bayesian Low-Tubal-Rank Robust Tensor Factorization with Multi-Rank Determination,
PAMI(43), No. 1, January 2021, pp. 62-76.
IEEE DOI 2012
Bayes methods, Principal component analysis, Adaptation models, Videos, Computational modeling, Sparse matrices, Robust PCA, Bayesian inference BibRef

Prijatelj, D.S.[Derek S.], McCurrie, M.[Mel], Anthony, S.E.[Samuel E.], Scheirer, W.J.[Walter J.],
A Bayesian evaluation framework for subjectively annotated visual recognition tasks,
PR(123), 2022, pp. 108395.
Elsevier DOI 2112
Uncertainty estimation, Epistemic uncertainty, Supervised learning, Bayesian inference, Bayesian modeling BibRef

Christmas, J.,
Non-stationary, online variational Bayesian learning, with circular variables,
PR(122), 2022, pp. 108340.
Elsevier DOI 2112
Online learning/processing, Variational methods, Bayes procedures BibRef

Khong, T.T.T.[Thi Thu Thao], Nakada, T.[Takashi], Nakashima, Y.[Yasuhiko],
Flexible Bayesian Inference by Weight Transfer for Robust Deep Neural Networks,
IEICE(E104-D), No. 11, November 2021, pp. 1981-1991.
WWW Link. 2112
BibRef

Manton, J.H., Le Bihan, N.,
On some global topological aspects of manifold learning,
ICIP17(225-229)
IEEE DOI 1803
Bayes methods, Dimensionality reduction, Geometry, Hyperspectral imaging, Image processing, Legged locomotion, manifold learning BibRef

Marghi, Y.M.[Yeganeh M.], Koçanaogullari, A.[Aziz], Akçakaya, M.[Murat], Erdogmus, D.[Deniz],
Active recursive Bayesian inference using Rényi information measures,
PRL(154), 2022, pp. 90-98.
Elsevier DOI 2202
Active learning, Recursive state estimation, Bayesian inference, Rényi entropy BibRef

Zhao, Y.[Yuan], Nassar, J.[Josue], Jordan, I.[Ian], Bugallo, M.[Mónica], Park, I.M.[Il Memming],
Streaming Variational Monte Carlo,
PAMI(45), No. 1, January 2023, pp. 1150-1161.
IEEE DOI 2212
Proposals, Monte Carlo methods, Bayes methods, State-space methods, Smoothing methods, Optimization, Time series analysis, Bayesian machine learning BibRef

Yu, H.[Hang], Wu, S.W.[Song-Wei], Dauwels, J.[Justin],
Efficient Variational Bayes Learning of Graphical Models With Smooth Structural Changes,
PAMI(45), No. 1, January 2023, pp. 475-488.
IEEE DOI 2212
Graphical models, Covariance matrices, Time series analysis, Bayes methods, Tuning, Brain modeling, Sparse matrices, inverse spectral density matrices BibRef

Salazar, A.[Addisson], Vergara, L.[Luis], Vidal, E.[Enrique],
A proxy learning curve for the Bayes classifier,
PR(136), 2023, pp. 109240.
Elsevier DOI 2301
Classification, Parameter learning, Sample size, Training set size, Probability of error BibRef

Zhang, X.Y.[Xin-Ying], Wang, T.[Tong], Wang, D.[Degen],
Fast Variational Bayesian Inference for Space-Time Adaptive Processing,
RS(15), No. 17, 2023, pp. 4334.
DOI Link 2310
BibRef

Flynn, H.[Hamish], Reeb, D.[David], Kandemir, M.[Melih], Peters, J.[Jan],
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison,
PAMI(45), No. 12, December 2023, pp. 15308-15327.
IEEE DOI 2311
BibRef

Wang, S.[Shihe], Ren, J.F.[Jian-Feng], Bai, R.[Ruibin], Yao, Y.[Yuan], Jiang, X.D.[Xu-Dong],
A Max-Relevance-Min-Divergence criterion for data discretization with applications on naive Bayes,
PR(149), 2024, pp. 110236.
Elsevier DOI 2403
Data discretization, Maximal dependency, Maximal relevance, Minimal divergence, Naive Bayes classification BibRef


Wang, S.[Shihe], Ren, J.F.[Jian-Feng], Lian, X.Y.[Xiao-Yu], Bai, R.B.[Rui-Bin], Jiang, X.D.[Xu-Dong],
Boosting the Discriminant Power of Naive Bayes,
ICPR22(4906-4912)
IEEE DOI 2212
Correlation, Codes, Benchmark testing, Feature extraction, Boosting, Data models, Numerical models BibRef

Fan, Y.H.[Yong-Hui], Wang, Y.L.[Ya-Lin],
Geometry-Aware Hierarchical Bayesian Learning on Manifolds,
WACV22(2743-2752)
IEEE DOI 2202
Manifolds, Representation learning, Point cloud compression, Convolution, Vision for Graphics BibRef

Yafune, R.[Ryoichiro], Sakuma, D.[Daisuke], Takayanagi, M.[Mirai], Tabei, Y.[Yasuo], Saito, N.[Noritaka], Saigo, H.[Hiroto],
Automatically mining Relevant Variable Interactions Via Sparse Bayesian Learning,
ICPR21(9635-9642)
IEEE DOI 2105
Support vector machines, Itemsets, Computational modeling, Predictive models, Probabilistic logic, Search problems, Prediction algorithms BibRef

Roth, W.[Wolfgang], Pernkopf, F.[Franz], Schindler, G.[Günther], Fröning, H.[Holger],
On Resource-Efficient Bayesian Network Classifiers and Deep Neural Networks,
ICPR21(10297-10304)
IEEE DOI 2105
Training, Deep learning, Quantization (signal), Neural networks, Memory management, Predictive models, Pareto optimization BibRef

Niu, C.[Chuang], Zhang, J.[Jun], Wang, G.[Ge], Liang, J.M.[Ji-Min],
GATCluster: Self-supervised Guassian-attention Network for Image Clustering,
ECCV20(XXV:735-751).
Springer DOI 2011
BibRef

Wang, H.[Hu], Chen, Y.H.[Yuan-Hong], Ma, C.[Congbo], Avery, J.[Jodie], Hull, L.[Louise], Carneiro, G.[Gustavo],
Multi-Modal Learning with Missing Modality via Shared-Specific Feature Modelling,
CVPR23(15878-15887)
IEEE DOI 2309
BibRef

Nguyen, C.[Cuong], Do, T.T.[Thanh-Toan], Carneiro, G.[Gustavo],
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference,
WACV20(3079-3089)
IEEE DOI 2006
Task analysis, Training, Bayes methods, Computational modeling, Uncertainty, Machine learning, Adaptation models BibRef

Wang, Q.L.[Qi-Long], Li, P.H.[Pei-Hua], Hu, Q.H.[Qing-Hua], Zhu, P.F.[Peng-Fei], Zuo, W.M.[Wang-Meng],
Deep Global Generalized Gaussian Networks,
CVPR19(5075-5083).
IEEE DOI 2002
BibRef

Cheng, Z.[Zezhou], Gadelha, M.[Matheus], Maji, S.[Subhransu], Sheldon, D.[Daniel],
A Bayesian Perspective on the Deep Image Prior,
CVPR19(5438-5446).
IEEE DOI 2002
BibRef

Shekhovtsov, A.[Alexander], Flach, B.[Boris],
Stochastic Normalizations as Bayesian Learning,
ACCV18(II:463-479).
Springer DOI 1906
BibRef

Akhtar, N., Mian, A., Porikli, F.M.[Fatih M.],
Joint Discriminative Bayesian Dictionary and Classifier Learning,
CVPR17(3919-3928)
IEEE DOI 1711
Atomic measurements, Bayes methods, Dictionaries, Machine learning, Mathematical model, Training, Training data BibRef

Katsuki, T., Inoue, M.,
Bayesian regression selecting valuable subset from mixed bag training data,
ICPR16(2580-2585)
IEEE DOI 1705
Algorithm design and analysis, Bayes methods, Data models, Supervised learning, Training, Training, data BibRef

Gao, X.G.[Xiao-Guang], Yang, Y.[Yu], Guo, Z.G.[Zhi-Gao], Chen, D.Q.[Da-Qing],
Bayesian approach to learn Bayesian networks using data and constraints,
ICPR16(3667-3672)
IEEE DOI 1705
Bayes methods, Estimation, Heuristic algorithms, Learning systems, Mathematical model, Parameter, estimation BibRef

Nie, S.Q.[Si-Qi], Zhao, Y.[Yue], Ji, Q.A.[Qi-Ang],
Latent regression Bayesian network for data representation,
ICPR16(3494-3499)
IEEE DOI 1705
Approximation algorithms, Bayes methods, Computational modeling, Data models, Inference algorithms, Linear, programming BibRef

Li, F.Y., Shafiee, M.J., Chung, A.G., Chwyl, B., Kazemzadeh, F., Wong, A., Zelek, J.,
High dynamic range map estimation via fully connected random fields with stochastic cliques,
ICIP15(2159-2163)
IEEE DOI 1512
Conditional Random Fields BibRef

Shafiee, M.J., Siva, P., Fieguth, P.W.[Paul W.], Wong, A.,
Efficient Deep Feature Learning and Extraction via StochasticNets,
Robust16(1101-1109)
IEEE DOI 1612
BibRef

Chung, A.G., Shafiee, M.J., Wong, A.,
Image Restoration via Deep-Structured Stochastically Fully-Connected Conditional Random Fields (DSFCRFs) for Very Low-Light Conditions,
CRV16(194-200)
IEEE DOI 1612
CRF BibRef

Shafiee, M.J., Chung, A.G., Wong, A., Fieguth, P.W.,
Improved fine structure modeling via guided stochastic clique formation in fully connected conditional random fields,
ICIP15(3260-3264)
IEEE DOI 1512
CRF BibRef

Shafiee, M.J., Wong, A.G., Siva, P., Fieguth, P.W.,
Efficient Bayesian inference using fully connected conditional random fields with stochastic cliques,
ICIP14(4289-4293)
IEEE DOI 1502
Computational modeling BibRef

Terzic, K.[Kasim], du Buf, J.M.H.,
An efficient Naive Bayes approach to category-level object detection,
ICIP14(1658-1662)
IEEE DOI 1502
Complexity theory BibRef

Escalante, H.J.[Hugo Jair], Sotomayor, M.[Mauricio], Montes, M.[Manuel], Lopez-Monroy, A.P.[A. Pastor],
Object Recognition with Näive Bayes-NN via Prototype Generation,
MCPR14(162-171).
Springer DOI 1407
BibRef

Cheng, D.Y.[Dong-Yang], Sun, T.F.[Tan-Feng], Jiang, X.H.[Xing-Hao], Wang, S.L.[Shi-Lin],
Unsupervised feature learning using Markov deep belief network,
ICIP13(260-264)
IEEE DOI 1402
Computational modeling BibRef

Tang, Y.[Yi], Srihari, S.N.[Sargur N.],
Efficient and accurate learning of Bayesian networks using chi-squared independence tests,
ICPR12(2723-2726).
WWW Link. 1302
BibRef

Staudenmaier, A.[Armin], Klauck, U.[Ulrich], Kreßel, U.[Ulrich], Lindner, F.[Frank], Wöhler, C.[Christian],
Confidence Measurements for Adaptive Bayes Decision Classifier Cascades and Their Application to Us Speed Limit Detection,
DAGM12(478-487).
Springer DOI 1209
BibRef

Takasu, A.[Atsuhiro], Fukagawa, D.[Daiji], Akutsu, T.[Tatsuya],
A Variational Bayesian EM Algorithm for Tree Similarity,
ICPR10(1056-1059).
IEEE DOI 1008
BibRef

Tong, Y.[Yan], Ji, Q.A.[Qi-Ang],
Learning Bayesian Networks with qualitative constraints,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Gomes, R.[Ryan], Welling, M.[Max], Perona, P.[Pietro],
Incremental learning of nonparametric Bayesian mixture models,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Jain, A.K., Mallapragada, P.K.[Pavan K.], Law, M.H.C.[Martin H.C.],
Bayesian Feedback in Data Clustering,
ICPR06(III: 374-378).
IEEE DOI 0609
BibRef

Martinez-Arroyo, M.[Miriam], Sucar, L.E.[L. Enrique],
Learning an Optimal Naive Bayes Classifier,
ICPR06(III: 1236-1239).
IEEE DOI 0609
BibRef
And: ICPR06(IV: 958).
IEEE DOI 0609
BibRef

Kanaujia, A.[Atul], Metaxas, D.N.[Dimitris N.],
Learning Multi-category Classification in Bayesian Framework,
ACCV06(I:255-264).
Springer DOI 0601

See also Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference. BibRef

Lo, B.P.L., Thiemjarus, S., Yang, G.Z.[Guang-Zhong],
Adaptive Bayesian networks for video processing,
ICIP03(I: 889-892).
IEEE DOI 0312
Adapt, or learn, while processing. BibRef

Fergus, R.[Rob], Perona, P.[Pietro], Zisserman, A.[Andrew],
A Sparse Object Category Model for Efficient Learning and Complete Recognition,
CLOR06(443-461).
Springer DOI 0711
BibRef
And:
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition,
CVPR05(I: 380-387).
IEEE DOI 0507
BibRef

Takebe, H.[Hiroaki], Kurokawa, K.[Koji], Katsuyama, Y.[Yutaka], Naoi, S.[Satoshi],
A Learning Pseudo Bayes Discriminant Method Based on Difference Distribution of Feature Vectors,
DAS02(134 ff.).
Springer DOI 0303
BibRef

Souafi-Bensafi, S., Parizeau, M., Le Bourgeois, F., Emptoz, H.,
Bayesian networks classifiers applied to documents,
ICPR02(I: 483-486).
IEEE DOI 0211
BibRef
Earlier:
Logical labeling using Bayesian networks,
ICDAR01(832-836).
IEEE DOI 0109
BibRef

Baesens, B., Egmont-Petersen, M., Castelo, R., Vanthienen, J.,
Learning Bayesian network classifiers for credit scoring using Markov chain Monte Carlo search,
ICPR02(III: 49-52).
IEEE DOI 0211
BibRef

Vailaya, A., Jain, A.K.,
Reject Option for VQ-based Bayesian Classification,
ICPR00(Vol II: 48-51).
IEEE DOI 0009
BibRef

Vailaya, A.[Aditya], Jain, A.K.[Anil K.],
Incremental Learning for Bayesian Classification of Images,
ICIP99(II:585-589).
IEEE DOI BibRef 9900

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Genetic Algorithms, Genetic Programming .


Last update:Mar 16, 2024 at 20:36:19