14.5.7 Bayesian Learning, Bayes Network, Bayesian Networks

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 Abstract.
WWW Version. 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 Abstract.
WWW Version. 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 Link 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.
WWW Version. 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.
WWW Version. 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 Abstract.
WWW Version. 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 Link 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. 0008

WWW Version. BibRef

Lampinen, J., Vehtari, A., Leinonen, K.,
Using Bayesian Neural Network to Solve the Inverse Problem in Electrical Impedance Tomography,
SCIA99(Neural Nets). BibRef 9900

Paulus, D., Hornegger, J., Niemann, H.,
A Framework for Statistical 3-D Object Recognition,
PRL(18), No. 11-13, November 1997, pp. 1153-1157.
Postscript Version. 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.
WWW Version. 0101
BibRef

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

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

Hornegger, J., Niemann, H.,
A Bayesian Approach to Learn and Classify 3D Objects from Intensity Images,
ICPR94(B:557-559).
IEEE DOI Link 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.
WWW Version. 0203
BibRef

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

Piro, P.[Paolo], Nock, R.[Richard], Nielsen, F.[Frank], Barlaud, M.[Michel],
Boosting Bayesian MAP Classification,
ICPR10(661-665).
IEEE DOI Link 1008
See also Multi-class Leveraged k-NN for Image Classification. 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 Abstract.
WWW Version. 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.
WWW Version. 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.
WWW Version. 0506
BibRef
Earlier:
Rapid spline-based kernel density estimation for bayesian networks,
ICPR04(III: 700-703).
IEEE DOI Link 0409
BibRef

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

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

Li, F.F.[Fei-Fei], Fergus, R.[Rob], Perona, P.[Pietro],
One-Shot Learning of Object Categories,
PAMI(28), No. 4, April 2006, pp. 594-611.
IEEE DOI Link 0604
BibRef
Earlier:
A bayesian approach to unsupervised one-shot learning of object categories,
ICCV03(1134-1141).
IEEE DOI Link 0311
BibRef

Wang, G.[Gang], Zhang, Y.[Ye], Li, F.F.[Fei-Fei],
Using Dependent Regions for Object Categorization in a Generative Framework,
CVPR06(II: 1597-1604).
IEEE DOI Link 0606
BibRef

Li, F.F.[Fei-Fei], Fergus, R.[Rob], Perona, P.[Pietro],
Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories,
CVIU(106), No. 1, April 2007, pp. 59-70.
WWW Version. 0704
BibRef
Earlier: GenModel04(178).
IEEE DOI Link 0406
BibRef

Object recognition; Categorization; Generative model; Incremental learning; Bayesian model

Li, F.F.[Fei-Fei], Perona, P.[Pietro],
A Bayesian Hierarchical Model for Learning Natural Scene Categories,
CVPR05(II: 524-531).
IEEE DOI Link 0507
BibRef

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

Ji, S.H.[Shi-Hao], Krishnapuram, B.[Balaji], Carin, L.[Lawrence],
Variational Bayes for Continuous Hidden Markov Models and Its Application to Active Learning,
PAMI(28), No. 4, April 2006, pp. 522-532.
IEEE DOI Link 0604
BibRef

Ji, S.H.[Shi-Hao], Carin, L.[Lawrence],
Cost-sensitive feature acquisition and classification,
PR(40), No. 5, May 2007, pp. 1474-1485.
WWW Version. 0702
Cost-sensitive classification; Partially observable Markov decision processes (POMDP); Hidden Markov models (HMMs); Variational Bayes (VB) BibRef

Ji, S.H.[Shi-Hao], Watson, L.T.[Layne T.], Carin, L.[Lawrence],
Semisupervised Learning of Hidden Markov Models via a Homotopy Method,
PAMI(31), No. 2, February 2009, pp. 275-287.
IEEE DOI Link 0901
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 Link 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 Link 0702
Feature vectors have missing features. Supervised regression algorithm. BibRef

Johansson, M., Olofsson, T.,
Bayesian Model Selection for Markov, Hidden Markov, and Multinomial Models,
SPLetters(14), No. 2, February 2007, pp. 129-132.
IEEE DOI Link 0703
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.
WWW Version. 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.
WWW Version. 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 Version. 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 Link 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 Link
WWW Version. 0907
Classification; Binary images; Bayesian networks; Variational inference BibRef

Schwier, J.M., Brooks, R.R., Griffin, C., Bukkapatnam, S.,
Zero knowledge hidden Markov model inference,
PRL(30), No. 14, 15 October 2009, pp. 1273-1280,.
Elsevier DOI Link
WWW Version. 0909
Pattern recognition; Hidden Markov model; Pattern discovery BibRef

Lowne, D.R., Roberts, S.J., Garnett, R.,
Sequential non-stationary dynamic classification with sparse feedback,
PR(43), No. 3, March 2010, pp. 897-905.
Elsevier DOI Link
WWW Version. 1001
Non-stationary dynamic classification; Sequential Bayesian learning; Missing data; Medical signal analysis; Brain-computer interface 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 Link
WWW Version. 1006
BibRef
Earlier: ICDAR09(1201-1205).
IEEE DOI Link 0907
BibRef
Earlier:
Classification and Automatic Annotation Extension of Images Using Bayesian Network,
SSPR08(937-946).
Springer DOI Link 0812
Probabilistic graphical models; Bayesian networks; Image classification; Image annotation; Semantic similarity; Wordnet; Visual features; Bayesian classifier 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 Link 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 Link 1112
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 Link
WWW Version. 1101
Dirichlet distribution; Generalized Dirichlet distribution; Naive Bayesian classifier; Prior distribution; Selective naive Bayes BibRef

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

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

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

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

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

Kanaujia, A.[Atul], Metaxas, D.[Dimitris],
Learning Multi-category Classification in Bayesian Framework,
ACCV06(I:255-264).
Springer DOI Link 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 Abstract. 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 Link 0711
BibRef
And:
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition,
CVPR05(I: 380-387).
IEEE DOI Link 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.).
HTML Version. 0303
BibRef

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

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

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

Utschick, W., Nossek, J.A.,
Bayesian Adaptation of Hidden Layers in Boolean Feedforward Neural Networks,
ICPR96(IV: 229-233).
IEEE DOI Link 9608
(Technical Univ. of Munich, D) BibRef

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