14.5.8 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.

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. 0008

DOI Link 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.
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

Pe˝a, J.M.[Jose Manuel], Lozano, J.A.[Jose Antonio], Larra˝aga, P.[Pedro], Inza, I.[I˝aki],
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

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

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

Fei-Fei, L.[Li], 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.
Elsevier DOI 0704
BibRef
Earlier: GenModel04(178).
IEEE DOI 0406
BibRef

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

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

Kuncheva, L.I.[Ludmila I.],
On the optimality of Na´ve 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

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 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.
Elsevier DOI 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 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 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

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 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.
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 Na´ve 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

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 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 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 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], 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

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

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.], AdŃo, 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], Bela´d, Y.[Yolande], Bela´d, 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


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., 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 Nńive 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], Kre▀el, U.[Ulrich], Lindner, F.[Frank], W÷hler, 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 13, 2017 at 16:25:24