14.2.16.1 Bayesian Clustering, Bayes Classifier

Forsyth, D.A., Haddon, J., Ioffe, S.,
The Joy of Sampling,
IJCV(41), No. 1-2, January-February 2001, pp. 109-134.
DOI Link Sampling for Bayesian models applied to structure from motion and color constancy. 0105
BibRef

Huang, H.J.[Hung-Ju], Hsu, C.N.[Chun-Nan],
Bayesian classification for data from the same unknown class,
SMC-B(32), No. 2, April 2002, pp. 137-145.
IEEE Top Reference. 0205
BibRef

Zribi, M.[Mourad],
Non-parametric and unsupervised Bayesian classification with Bootstrap sampling,
IVC(22), No. 1, January 2004, pp. 1-8.
Elsevier DOI 0401
BibRef

Zribi, M.[Mourad],
Unsupervised Bayesian image segmentation using orthogonal series,
JVCIR(18), No. 6, December 2007, pp. 496-503.
Elsevier DOI 0711
Unsupervised Bayesian image segmentation; Orthogonal series estimator; Stochastic and Nonparametric Expectation-Maximization BibRef

Zribi, M.[Mourad], Ghorbel, F.[Faouzi],
An Unsupervised and Non-Parametric Bayesian Classifier,
PRL(24), No. 1-3, January 2003, pp. 97-112.
Elsevier DOI 0211
BibRef
Earlier:
An unsupervised and non-parametric Bayesian Image segmentation,
CIAP95(423-428).
Springer DOI 9509
BibRef

Vass, G.G.[György G.], Daoudi, M.[Mohamed], Ghorbel, F.[Faouzi],
Optimization methods in multilayer classifier networks for automatic control of lamellibranch larva growth,
CIAP97(II: 220-227).
Springer DOI 9709
BibRef

Eastman, J.R., Laney, R.M.,
Bayesian Soft Classification for Sub-Pixel Analysis: A Critical Evaluation,
PhEngRS(68), No. 11, November 2002, pp. 1149-1154. Fuzzy training sites improve the accuracy of the Bayesian classification procedure by increasing the degree of overlap between parent distributions.
WWW Link. 0304
BibRef

Pernkopf, F.[Franz], O'Leary, P.[Paul],
Floating search algorithm for structure learning of Bayesian network classifiers,
PRL(24), No. 15, November 2003, pp. 2839-2848.
Elsevier DOI 0308
BibRef
Earlier:
Feature Selection for Classification Using Genetic Algorithms with a Novel Encoding,
CAIP01(161 ff.).
Springer DOI 0210
BibRef

Pernkopf, F.[Franz],
Bayesian network classifiers versus selective k-NN classifier,
PR(38), No. 1, January 2005, pp. 1-10.
Elsevier DOI 0410
BibRef

Pernkopf, F.[Franz], Wohlmayr, M.[Michael], Tschiatschek, S.[Sebastian],
Maximum Margin Bayesian Network Classifiers,
PAMI(34), No. 3, March 2012, pp. 521-532.
IEEE DOI 1201
Conjugate gradient optimization. Maintain normalization on constraints of the Bayesian network BibRef

Mutsam, N.[Nikolaus], Pernkopf, F.[Franz],
Maximum margin hidden Markov models for sequence classification,
PRL(77), No. 1, 2016, pp. 14-20.
Elsevier DOI 1606
Hidden Markov models BibRef

Tschiatschek, S., Pernkopf, F.,
On Bayesian Network Classifiers with Reduced Precision Parameters,
PAMI(37), No. 4, April 2015, pp. 774-785.
IEEE DOI 1503
Bayes methods BibRef

Pernkopf, F.[Franz], Wohlmayr, M.[Michael],
Stochastic margin-based structure learning of Bayesian network classifiers,
PR(46), No. 2, February 2013, pp. 464-471.
Elsevier DOI 1210
Bayesian network classifier; Discriminative learning; Maximum margin learning; Structure learning BibRef

Raymer, M.L., Doom, T.E., Kuhn, L.A., Punch, W.F.,
Knowledge discovery in medical and biological datasets using a hybrid Bayes classifier/evolutionary algorithm,
SMC-B(33), No. 5, October 2003, pp. 802-813.
IEEE Abstract. 0310
BibRef

Thomaz, C.E., Gillies, D.F., Feitosa, R.Q.,
A New Covariance Estimate for Bayesian Classifiers in Biometric Recognition,
CirSysVideo(14), No. 2, February 2004, pp. 214-223.
IEEE Abstract. 0403
BibRef

Storvik, G., Fjortoft, R., Solberg, A.H.S.,
A Bayesian Approach to Classification of Multiresolution Remote Sensing Data,
GeoRS(43), No. 3, March 2005, pp. 539-547.
IEEE Abstract. 0501
BibRef

Kwoh, C.K.[Chee-Keong], Gillies, D.F.[Duncan Fyfe],
Estimating the initial values of unobservable variables in visual probabilistic networks,
CAIP95(326-333).
Springer DOI 9509
BibRef

Aksoy, S., Koperski, K., Tusk, C., Marchisio, G., Tilton, J.C.,
Learning Bayesian Classifiers for Scene Classification With a Visual Grammar,
GeoRS(43), No. 3, March 2005, pp. 581-589.
IEEE Abstract. 0501
BibRef

Hand, D.J., and Yu, K.,
Idiot's Bayes: Not so Stupid After All?,
Statistical Review(69), 2001, pp. 385-398. BibRef 0100

Jamain, A.[Adrien], Hand, D.J.[David J.],
The Naive Bayes Mystery: A classification detective story,
PRL(26), No. 11, August 2005, pp. 1752-1760.
Elsevier DOI 0506
BibRef

Zhang, H.[Harry], Su, J.[Jiang],
Learning probabilistic decision trees for AUC,
PRL(27), No. 8, June 2006, pp. 892-899.
Elsevier DOI Naive Bayes; Ranking 0605
BibRef

Nadarajah, S.[Saralees], Kotz, S.[Samuel],
A comment on: 'A one-dimensional analysis for the probability of error of linear classifiers for normally distributed classes' by Rueda,
PR(40), No. 5, May 2007, pp. 1632-1633.
Elsevier DOI 0702

See also one-dimensional analysis for the probability of error of linear classifiers for normally distributed classes, A. BibRef

Shi, X.J.[Xiao-Jin], Manduchi, R.[Roberto],
On the Bayes fusion of visual features,
IVC(25), No. 11, 1 November 2007, pp. 1748-1758.
Elsevier DOI 0709
Image classification; Bayes fusion; Color; Texture BibRef

Sicard, R.[Rudy], Artieres, T.[Thierry], Petit, E.[Eric],
Learning iteratively a classifier with the Bayesian Model Averaging Principle,
PR(41), No. 3, March 2008, pp. 930-938.
Elsevier DOI 0711
Bayesian model averaging; Point estimate approximation; Naieve Bayes classifier; Statistical classification BibRef

Hernandez-Lobato, D.[Daniel], Hernandez-Lobato, J.M.[Jose Miguel],
Bayes Machines for binary classification,
PRL(29), No. 10, 15 July 2008, pp. 1466-1473.
Elsevier DOI 0711
Kernel methods; Approximate inference; Bayesian methods; Expectation Propagation; Bayes Point Machines; Bayes Machines BibRef

Agrawal, R.K., Bala, R.[Rajni],
Incremental Bayesian classification for multivariate normal distribution data,
PRL(29), No. 13, 1 October 2008, pp. 1873-1876.
Elsevier DOI 0804
Bayesian classification; Multivariate normal distribution; Sequential classification; Feature selection BibRef

Agrawal, R.K., Bala, R.[Rajni], Bala, M.[Manju],
Discriminant Function Revisited for Incremental Learning,
ICCVGIP08(435-441).
IEEE DOI 0812
BibRef

Damoulas, T.[Theodoros], Girolami, M.A.[Mark A.],
Pattern recognition with a Bayesian kernel combination machine,
PRL(30), No. 1, 1 January 2009, pp. 46-54.
Elsevier DOI 0811
Classification; Kernel combination; MCMC; Probit regression; Bayesian inference; Information integration BibRef

Damoulas, T.[Theodoros], Girolami, M.A.[Mark A.],
Combining feature spaces for classification,
PR(42), No. 11, November 2009, pp. 2671-2683.
Elsevier DOI 0907
Variational Bayes approximation; Multiclass classification; Kernel combination; Hierarchical Bayes; Bayesian inference; Ensemble learning; Multi-modal modelling; Information integration BibRef

Liao, W.H.[Wen-Hui], Ji, Q.A.[Qi-Ang],
Learning Bayesian network parameters under incomplete data with domain knowledge,
PR(42), No. 11, November 2009, pp. 3046-3056.
Elsevier DOI 0907
BibRef
Earlier:
Exploiting qualitative domain knowledge for learning Bayesian network parameters with incomplete data,
ICPR08(1-4).
IEEE DOI 0812
Bayesian network parameter learning; Missing data; EM algorithm; Facial action unit (AU) recognition BibRef

Jiang, L.X.[Liang-Xiao],
Random one-dependence estimators,
PRL(32), No. 3, 1 February 2011, pp. 532-539.
Elsevier DOI 1101
Naive Bayes; One-dependence estimators; Random selection; Classification; Class probability estimation; Ranking BibRef

Schlueter, R.[Ralf], Nussbaum-Thom, M.[Markus], Ney, H.[Hermann],
Does the Cost Function Matter in Bayes Decision Rule?,
PAMI(34), No. 2, February 2012, pp. 292-301.
IEEE DOI 1112
Applied to various tasks. Analysis of Bayesian techniques. BibRef

Silva, J.F.[Jorge F.], Narayanan, S.S.[Shrikanth S.],
On signal representations within the Bayes decision framework,
PR(45), No. 5, May 2012, pp. 1853-1865.
Elsevier DOI 1201
Signal representation; Minimum risk decision; Bayes decision framework; Estimation-approximation error tradeoff; complexity regularization; Mutual information; Decision trees; Linear discriminant analysis BibRef

Ducinskas, K.[Kestutis], Stabingiene, L.[Lijana], Stabingis, G.[Giedrius],
Image Classification Based on Bayes Discriminant Functions,
Procedia Env. Sci(7), 2011, pp. 218-223
Elsevier DOI 1301
BibRef
And: Retraction information for second reference:
Application of Bayes linear discriminant functions in image classification,
PRL(35), No. 3, 1 February 2013, pp. 358.
Elsevier DOI 1301
Retracted Reference, do not use: PRL(33), No. 3, 1 February 2012, pp. 278-282. Image classification; Gaussian random fields; Actual error rate; Bayes discriminant function. Adding spatial information. BibRef

Ružic, T.[Tijana], Pižurica, A.[Aleksandra], Philips, W.[Wilfried],
Neighborhood-consensus message passing as a framework for generalized iterated conditional expectations,
PRL(33), No. 3, 1 February 2012, pp. 309-318.
Elsevier DOI 1201
Markov random fields; Bayesian inference; Iterated conditional modes; Message passing BibRef

Liao, W.Z.[Wen-Zhi], Pizurica, A.[Aleksandra], Philips, W.[Wilfried], Pi, Y.[Youguo],
A fast iterative kernel PCA feature extraction for hyperspectral images,
ICIP10(1317-1320).
IEEE DOI 1009
BibRef

Boullé, M.[Marc],
Functional data clustering via piecewise constant nonparametric density estimation,
PR(45), No. 12, December 2012, pp. 4389-4401.
Elsevier DOI 1208
Functional data; Distributional data; Exploratory analysis; Clustering; Bayesianism; Model selection; Density estimation BibRef

Zheng, S.F.[Song-Feng], Liu, W.X.[Wei-Xiang],
Functional gradient ascent for Probit regression,
PR(45), No. 12, December 2012, pp. 4428-4437.
Elsevier DOI 1208
Probit regression; Classification; Functional gradient ascent; Boosting BibRef

Dalton, L.A.[Lori A.], Dougherty, E.R.[Edward R.],
Optimal classifiers with minimum expected error within a Bayesian framework - Part I: Discrete and Gaussian models,
PR(46), No. 5, May 2013, pp. 1301-1314.
Elsevier DOI 1302
Bayesian estimation; Classification; Error estimation; Genomics; Minimum mean-square estimation; Small samples BibRef

Dalton, L.A.[Lori A.], Dougherty, E.R.[Edward R.],
Optimal classifiers with minimum expected error within a Bayesian framework - Part II: Properties and performance analysis,
PR(46), No. 5, May 2013, pp. 1288-1300.
Elsevier DOI 1302
Bayesian estimation; Classification; Error estimation; Genomics; Minimum mean-square estimation; Small samples BibRef

Nielsen, F.,
An Information-Geometric Characterization of Chernoff Information,
SPLetters(20), No. 3, March 2013, pp. 269-272.
IEEE DOI 1303
BibRef

Trajdos, P.[Pawel], Burduk, R.[Robert],
Linear classifier combination via multiple potential functions,
PR(111), 2021, pp. 107681.
Elsevier DOI 2012
Linear classifier, Potential function, Ensemble of classifiers, Score function BibRef

Feng, G.[Guang], Zhang, J.D.[Jia-Dong], Liao, S.S.Y.[Stephen Shao-Yi],
A novel method for combining Bayesian networks, theoretical analysis, and its applications,
PR(47), No. 5, 2014, pp. 2057-2069.
Elsevier DOI 1402
Bayesian networks combination BibRef

Wang, X.Z.[Xi-Zhao], He, Y.L.[Yu-Lin], Wang, D.D.,
Non-Naive Bayesian Classifiers for Classification Problems With Continuous Attributes,
Cyber(44), No. 1, January 2014, pp. 21-39.
IEEE DOI 1402
Bayes methods BibRef

Besson, O., Dobigeon, N., Tourneret, J.Y.,
Joint Bayesian Estimation of Close Subspaces from Noisy Measurements,
SPLetters(21), No. 2, February 2014, pp. 168-171.
IEEE DOI 1402
Bayes methods BibRef

Ruiz, P., Mateos, J., Camps-Valls, G., Molina, R., Katsaggelos, A.K.,
Bayesian Active Remote Sensing Image Classification,
GeoRS(52), No. 4, April 2014, pp. 2186-2196.
IEEE DOI 1403
Bayes methods BibRef

Stein, M., Castaneda, M., Mezghani, A., Nossek, J.A.,
Information-Preserving Transformations for Signal Parameter Estimation,
SPLetters(21), No. 7, July 2014, pp. 866-870.
IEEE DOI 1405
Bayes methods BibRef

Knowles, D.A., Ghahramani, Z.,
Pitman-Yor Diffusion Trees for Bayesian Hierarchical Clustering,
PAMI(37), No. 2, February 2015, pp. 271-289.
IEEE DOI 1502
Bayes methods BibRef

Archambeau, C.[Cedric], Lakshminarayanan, B., Bouchard, G.,
Latent IBP Compound Dirichlet Allocation,
PAMI(37), No. 2, February 2015, pp. 321-333.
IEEE DOI 1502
Analytical models Indian buffet process (IBP). BibRef

Gershman, S.J., Frazier, P.I., Blei, D.M.,
Distance Dependent Infinite Latent Feature Models,
PAMI(37), No. 2, February 2015, pp. 334-345.
IEEE DOI 1502
Analytical models BibRef

Foti, N.J., Williamson, S.A.,
A Survey of Non-Exchangeable Priors for Bayesian Nonparametric Models,
PAMI(37), No. 2, February 2015, pp. 359-371.
IEEE DOI 1502
Bayesian nonparametrics BibRef

Doshi-Velez, F., Pfau, D., Wood, F., Roy, N.,
Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning,
PAMI(37), No. 2, February 2015, pp. 394-407.
IEEE DOI 1502
Bayes methods BibRef

Deisenroth, M.P., Fox, D., Rasmussen, C.E.,
Gaussian Processes for Data-Efficient Learning in Robotics and Control,
PAMI(37), No. 2, February 2015, pp. 408-423.
IEEE DOI 1502
Approximation methods BibRef

Gilboa, E., Saatci, Y., Cunningham, J.P.,
Scaling Multidimensional Inference for Structured Gaussian Processes,
PAMI(37), No. 2, February 2015, pp. 424-436.
IEEE DOI 1502
Additives BibRef

Orbanz, P., Roy, D.M.,
Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures,
PAMI(37), No. 2, February 2015, pp. 437-461.
IEEE DOI 1502
Analytical models BibRef

Palla, K., Knowles, D.A., Ghahramani, Z.,
Relational Learning and Network Modelling Using Infinite Latent Attribute Models,
PAMI(37), No. 2, February 2015, pp. 462-474.
IEEE DOI 1502
Atmospheric modeling BibRef

Xu, Z., Yan, F., Qi, Y.,
Bayesian Nonparametric Models for Multiway Data Analysis,
PAMI(37), No. 2, February 2015, pp. 475-487.
IEEE DOI 1502
Bayes methods BibRef

Blomstedt, P., Tang, J., Xiong, J., Granlund, C., Corander, J.,
A Bayesian Predictive Model for Clustering Data of Mixed Discrete and Continuous Type,
PAMI(37), No. 3, March 2015, pp. 489-498.
IEEE DOI 1502
Bayes methods BibRef

Sun, S.J.[Shu-Jin], Zhong, P.[Ping], Xiao, H.T.[Huai-Tie], Wang, R.S.[Run-Sheng],
Active Learning With Gaussian Process Classifier for Hyperspectral Image Classification,
GeoRS(53), No. 4, April 2015, pp. 1746-1760.
IEEE DOI 1502
Bayes methods BibRef

Sun, L.[Lei], Toh, K.A.[Kar-Ann], Lin, Z.P.[Zhi-Ping],
A center sliding Bayesian binary classifier adopting orthogonal polynomials,
PR(48), No. 6, 2015, pp. 2013-2028.
Elsevier DOI 1503
Binary classification BibRef

Klarreich, E.[Erica],
In Search of Bayesian Inference,
CACM(58), No. 1, January 2015, pp. 21-24.
DOI Link 1503
BibRef

Broumand, A.[Ariana], Esfahani, M.S.[Mohammad Shahrokh], Yoon, B.J.[Byung-Jun], Dougherty, E.R.[Edward R.],
Discrete optimal Bayesian classification with error-conditioned sequential sampling,
PR(48), No. 11, 2015, pp. 3766-3782.
Elsevier DOI 1506
Optimal Bayesian classifier BibRef

El Korso, M.N., Boyer, R., Larzabal, P., Fleury, B.H.,
Estimation Performance for the Bayesian Hierarchical Linear Model,
SPLetters(23), No. 4, April 2016, pp. 488-492.
IEEE DOI 1604
Bayes methods BibRef

Ruiz, P.[Pablo], Molina, R.[Rafael], Katsaggelos, A.K.[Aggelos K.],
Joint Data Filtering and Labeling Using Gaussian Processes and Alternating Direction Method of Multipliers,
IP(25), No. 7, July 2016, pp. 3059-3072.
IEEE DOI 1606
Bayes methods BibRef

Rohani, N.[Neda], Ruiz, P.[Pablo], Molina, R.[Rafael], Katsaggelos, A.K.[Aggelos K.],
Variational Gaussian process for multisensor classification problems,
PRL(116), 2018, pp. 80-87.
Elsevier DOI 1812
Fusion, Gaussian process, Variational inference, Kernel, Posterior probability BibRef

Morales-Álvarez, P.[Pablo], Pérez-Suay, A.[Adrián], Molina, R.[Rafael], Camps-Valls, G.[Gustau],
Remote Sensing Image Classification With Large-Scale Gaussian Processes,
GeoRS(56), No. 2, February 2018, pp. 1103-1114.
IEEE DOI 1802
Computational efficiency, Gaussian processes, Image resolution, Kernel, Remote sensing, Standards, Support vector machines, variational inference BibRef

Svendsen, D.H.[Daniel Heestermans], Martino, L.[Luca], Camps-Valls, G.[Gustau],
Active emulation of computer codes with Gaussian processes: Application to remote sensing,
PR(100), 2020, pp. 107103.
Elsevier DOI 2005
Active learning, Gaussian process, Emulation, Design of experiments, Computer code, Remote sensing, Radiative transfer model BibRef

Ruiz, P.[Pablo], Morales-Álvarez, P.[Pablo], Molina, R.[Rafael], Katsaggelos, A.K.[Aggelos K.],
Learning from crowds with variational Gaussian processes,
PR(88), 2019, pp. 298-311.
Elsevier DOI 1901
Crowdsourcing, Classification, Gaussian processes, Bayesian modeling, Variational inference BibRef

Ruiz, P.[Pablo], Mateos, J.[Javier], Molina, R.[Rafael], Katsaggelos, A.K.[Aggelos K.],
Learning filters in Gaussian process classification problems,
ICIP14(2913-2917)
IEEE DOI 1502
Bayes methods BibRef

Morales-Álvarez, P.[Pablo], Pérez-Suay, A.[Adrián], Molina, R.[Rafael], Camps-Valls, G.[Gustau], Katsaggelos, A.K.,
Passive millimeter wave image classification with large scale Gaussian processes,
ICIP17(370-374)
IEEE DOI 1803
Bayes methods, Gaussian processes, image classification, image resolution, inference mechanisms, variational inference BibRef

Serra, J.G., Ruiz, P.[Pablo], Molina, R.[Rafael], Katsaggelos, A.K.[Aggelos K.],
Bayesian logistic regression with sparse general representation prior for multispectral image classification,
ICIP16(1893-1897)
IEEE DOI 1610
Adaptation models BibRef

Tang, B., Kay, S., He, H., Baggenstoss, P.M.,
EEF: Exponentially Embedded Families with Class-Specific Features for Classification,
SPLetters(23), No. 7, July 2016, pp. 969-973.
IEEE DOI 1608
Bayes methods. Class specific features, not the general set. BibRef

Kong, G.G.[Gang-Gang], Jiang, L.X.[Liang-Xiao], Li, C.Q.[Chao-Qun],
Beyond accuracy: Learning selective Bayesian classifiers with minimal test cost,
PRL(80), No. 1, 2016, pp. 165-171.
Elsevier DOI 1609
Naive Bayes BibRef

Oneto, L.[Luca], Anguita, D.[Davide], Ridella, S.[Sandro],
PAC-bayesian analysis of distribution dependent priors: tighter risk bounds and stability analysis,
PRL(80), No. 1, 2016, pp. 200-207.
Elsevier DOI 1609
PAC-Bayes BibRef

Wong, T.T.[Tzu-Tsung], Liu, C.R.[Chao-Rui],
An efficient parameter estimation method for generalized Dirichlet priors in naïve Bayesian classifiers with multinomial models,
PR(60), No. 1, 2016, pp. 62-71.
Elsevier DOI 1609
Covariance matrix BibRef

Darwish, S.M.,
Combining firefly algorithm and Bayesian classifier: New direction for automatic multilabel image annotation,
IET-IPR(10), No. 10, 2016, pp. 763-772.
DOI Link 1610
Bayes methods BibRef

Šuch, O.[Ondrej], Barreda, S.[Santiago],
Bayes covariant multi-class classification,
PRL(84), No. 1, 2016, pp. 99-106.
Elsevier DOI 1612
Multi-class classification BibRef

Mariooryad, S., Busso, C.,
The Cost of Dichotomizing Continuous Labels for Binary Classification Problems: Deriving a Bayesian-Optimal Classifier,
AffCom(8), No. 1, January 2017, pp. 119-130.
IEEE DOI 1703
Bayes methods BibRef

Pereyra, M.[Marcelo],
Maximum-a-Posteriori Estimation with Bayesian Confidence Regions,
SIIMS(10), No. 1, 2017, pp. 285-302.
DOI Link 1704
BibRef

Lázaro, M.[Marcelino], Hayes, M.H.[Monson H.], Figueiras-Vidal, A.R.[Aníbal R.],
Training neural network classifiers through Bayes risk minimization applying unidimensional Parzen windows,
PR(77), 2018, pp. 204-215.
Elsevier DOI 1802
Bayes risk, Parzen windows, Binary classification BibRef

Durmus, A.[Alain], Moulines, E.[Eric], Pereyra, M.[Marcelo],
Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau,
SIIMS(11), No. 1, 2018, pp. 473-506.
DOI Link 1804
BibRef

Nguyen, T.T.T.[Thi Thu Thuy], Nguyen, T.T.[Tien Thanh], Liew, A.W.C.[Alan Wee-Chung], Wang, S.L.[Shi-Lin],
Variational inference based bayes online classifiers with concept drift adaptation,
PR(81), 2018, pp. 280-293.
Elsevier DOI 1806
Online learning, Variational inference, Bayesian classifier, Data stream, Concept drift BibRef

Zhao, T.Y.[Tian-Yi], Zhang, B.P.[Bao-Peng], He, M.[Ming], Zhang, W.[Wei], Zhou, N.[Ning], Yu, J.[Jun], Fan, J.P.[Jian-Ping],
Embedding Visual Hierarchy With Deep Networks for Large-Scale Visual Recognition,
IP(27), No. 10, October 2018, pp. 4740-4755.
IEEE DOI 1808
Bayes methods, image classification, learning (artificial intelligence), mixture models, Bayesian approach BibRef

Zhao, T.Y.[Tian-Yi], Chen, Q.Y.[Qiu-Yu], Kuang, Z.Z.[Zhen-Zhong], Yu, J.[Jun], Zhang, W.[Wei], Fan, J.P.[Jian-Ping],
Deep Mixture of Diverse Experts for Large-Scale Visual Recognition,
PAMI(41), No. 5, May 2019, pp. 1072-1087.
IEEE DOI 1904
Task analysis, Visualization, Training, Complexity theory, Image recognition, Prediction algorithms, Diversity reception, large-scale visual recognition BibRef

Zhao, Z.C.[Zhen-Chong], Wang, X.D.[Xiao-Dan],
Multi-segments Naïve Bayes classifier in likelihood space,
IET-CV(12), No. 6, September 2018, pp. 882-891.
DOI Link 1808
BibRef

Fan, W.T.[Wen-Tao], Bouguila, N.[Nizar], Bourouis, S.[Sami], Laalaoui, Y.[Yacine],
Entropy-based variational Bayes learning framework for data clustering,
IET-IPR(12), No. 10, October 2018, pp. 1762-1772.
DOI Link 1809
BibRef

Lagrange, A.[Adrien], Fauvel, M.[Mathieu], May, S.[Stéphane], Dobigeon, N.[Nicolas],
Hierarchical Bayesian image analysis: From low-level modeling to robust supervised learning,
PR(85), 2019, pp. 26-36.
Elsevier DOI 1810
Bayesian model, Supervised learning, Image interpretation, Markov random field BibRef

Wang, D.[Dong], Song, G.[Ge], Tan, X.Y.[Xiao-Yang],
Bayesian denoising hashing for robust image retrieval,
PR(86), 2019, pp. 134-142.
Elsevier DOI 1811
Image retrieval, Denoising hashing, Probabilistic model, Variational Bayes BibRef

Gezici, S., Varshney, P.K.,
On the Optimality of Likelihood Ratio Test for Prospect Theory-Based Binary Hypothesis Testing,
SPLetters(25), No. 12, December 2018, pp. 1845-1849.
IEEE DOI 1812
Bayes methods, decision making, decision theory, statistical testing, optimality, likelihood ratio test, randomization BibRef

Xu, Y., Hong, X., Porikli, F.M.[Fatih M.], Liu, X., Chen, J., Zhao, G.,
Saliency Integration: An Arbitrator Model,
MultMed(21), No. 1, January 2019, pp. 98-113.
IEEE DOI 1901
Computational modeling, Bayes methods, Estimation, Adaptation models, Predictive models, Biological system modeling, arbitrator model BibRef

Jiang, L.X.[Liang-Xiao], Zhang, L.G.[Lun-Gan], Yu, L.J.[Liang-Jun], Wang, D.H.[Dian-Hong],
Class-specific attribute weighted naive Bayes,
PR(88), 2019, pp. 321-330.
Elsevier DOI 1901
Naive Bayes, Attribute weighting, Weight optimization BibRef

Ali, M.[Muhammad], Gao, J.B.[Jun-Bin], Antolovich, M.[Michael],
Parametric Classification of Bingham Distributions Based on Grassmann Manifolds,
IP(28), No. 12, December 2019, pp. 5771-5784.
IEEE DOI 1909
Manifolds, Kernel, Data models, Bayes methods, Parametric statistics, Maximum likelihood estimation, Analytical models, classification BibRef

Kuang, W., Chan, Y., Tsang, S., Siu, W.,
Online-Learning-Based Bayesian Decision Rule for Fast Intra Mode and CU Partitioning Algorithm in HEVC Screen Content Coding,
IP(29), No. 1, 2020, pp. 170-185.
IEEE DOI 1910
Bayes methods, computational complexity, learning (artificial intelligence), video coding, scene change detection BibRef

Hassan, S.S.[Syeda Sakira], Huttunen, H.[Heikki], Niemi, J.[Jari], Tohka, J.[Jussi],
Bayesian receiver operating characteristic metric for linear classifiers,
PRL(128), 2019, pp. 52-59.
Elsevier DOI 1912
Receiver operating characteristic curve, Bayesian error estimation, Classification BibRef

Rademacher, P., Wagner, K.,
Efficient Bayesian Sequential Classification Under the Markov Assumption for Various Loss Functions,
SPLetters(27), 2020, pp. 401-405.
IEEE DOI 2004
Markov processes, Viterbi algorithm, Signal processing algorithms, Hidden Markov models, efficient computation BibRef

Zhou, Q.P.[Qing-Ping], Yu, T.C.[Teng-Chao], Zhang, X.Q.[Xiao-Qun], Li, J.L.[Jing-Lai],
Bayesian Inference and Uncertainty Quantification for Medical Image Reconstruction with Poisson Data,
SIIMS(13), No. 1, 2020, pp. 29-52.
DOI Link 2004
BibRef

Kim, H.C.[Hae-Cheon], Park, J.H.[Jin-Hyeong], Kim, D.W.[Dae-Won], Lee, J.[Jaesung],
Multilabel Naïve Bayes classification considering label dependence,
PRL(136), 2020, pp. 279-285.
Elsevier DOI 2008
Multilabel classifier, Naïve Bayes classification, Label dependence BibRef

Zhang, H.[Huan], Jiang, L.X.[Liang-Xiao], Yu, L.J.[Liang-Jun],
Attribute and instance weighted naive Bayes,
PR(111), 2021, pp. 107674.
Elsevier DOI 2012
Naive Bayes, Attribute weighting, Instance weighting, Eager learning, Lazy learning BibRef

Carlucci, F.M.[Fabio Maria], Porzi, L.[Lorenzo], Caputo, B.[Barbara], Ricci, E.[Elisa], Buló, S.R.[Samuel Rota],
MultiDIAL: Domain Alignment Layers for (Multisource) Unsupervised Domain Adaptation,
PAMI(43), No. 12, December 2021, pp. 4441-4452.
IEEE DOI 2112
BibRef
Earlier:
AutoDIAL: Automatic Domain Alignment Layers,
ICCV17(5077-5085)
IEEE DOI 1802
BibRef
And:
Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation,
CIAP17(I:357-369).
Springer DOI 1711
Deep learning, Adaptation models, Training data, Visualization, Entropy, Data models, entropy loss. learning (artificial intelligence), pattern classification, AutoDIAL, automatic domain alignment layers, classifier training, Visualization BibRef

Kuzborskij, I.[Ilja], Carlucci, F.M.[Fabio Maria], Caputo, B.[Barbara],
When Naive Bayes Nearest Neighbors Meet Convolutional Neural Networks,
CVPR16(2100-2109)
IEEE DOI 1612
BibRef

Byvshev, P.[Petr], Mettes, P.S.[Pascal S.], Xiao, Y.[Yu],
Are 3D convolutional networks inherently biased towards appearance?,
CVIU(220), 2022, pp. 103437.
Elsevier DOI 2206
3D models, Temporality measure, Motion analysis, Large-scale videosets BibRef

de la Riva, M., Mettes, P.S.[Pascal S.],
Bayesian 3D ConvNets for Action Recognition from Few Examples,
MDALC19(1337-1343)
IEEE DOI 2004
Bayes methods, belief networks, convolutional neural nets, image colour analysis, image motion analysis, image recognition, action recognition BibRef

Zhang, H.[Huan], Jiang, L.[Liangxiao], Webb, G.I.[Geoffrey I.],
Rigorous non-disjoint discretization for naive Bayes,
PR(140), 2023, pp. 109554.
Elsevier DOI 2305
Naive Bayes, Singleton interval, Proportional weighting, Discretization BibRef

Lefebvre, T.[Tom], Crevecoeur, G.[Guillaume],
A posteriori control densities: Imitation learning from partial observations,
PRL(169), 2023, pp. 87-94.
Elsevier DOI 2305
Information-theory, Hidden markov models, Bayesian methods, Imitation learning, Markov decision processes BibRef

Du, X.[Xinqi], Chen, H.C.[He-Chang], Wang, C.[Che], Xing, Y.H.[Yong-Heng], Yang, J.[Jielong], Yu, P.S.[Philip S.], Chang, Y.[Yi], He, L.F.[Li-Fang],
Robust multi-agent reinforcement learning via Bayesian distributional value estimation,
PR(145), 2024, pp. 109917.
Elsevier DOI 2311
Multi-agent reinforcement learning, Bayesian inference, Distributional value function, Deep reinforcement learning BibRef

Khodayari-Samghabadi, I.[Imaneh], Mohammad-Khanli, L.[Leyli], Tanha, J.[Jafar],
A Fast Multi-Network K-Dependence Bayesian Classifier for Continuous Features,
PR(150), 2024, pp. 110299.
Elsevier DOI 2403
Kernel density estimation, B-spline functions, Probability density estimation, Continuous features, Bayesian network classifier BibRef

Cai, Z.[Ziruo], Tang, J.Q.[Jun-Qi], Mukherjee, S.[Subhadip], Li, J.L.[Jing-Lai], Schonlieb, C.B.[Carola-Bibiane], Zhang, X.Q.[Xiao-Qun],
NF-ULA: Normalizing Flow-Based Unadjusted Langevin Algorithm for Imaging Inverse Problems,
SIIMS(17), No. 2, 2024, pp. 820-860.
DOI Link 2407
BibRef

Matuk, J.[James], Bharath, K.[Karthik], Chkrebtii, O.[Oksana], Kurtek, S.[Sebastian],
Geometric empirical Bayesian model for classification of functional data under diverse sampling regimes,
Diff-CVML21(4429-4437)
IEEE DOI 2109
Phase measurement, Computational modeling, Training data, Data models, Bayes methods BibRef

Jalali, H.[Hamed], Kasneci, G.[Gjeraji],
Aggregating Dependent Gaussian Experts in Local Approximation,
ICPR21(9015-9022)
IEEE DOI 2105
Training, Graphical models, Estimation, Gaussian processes, Bayes methods, Approximation methods BibRef

Berns, F.[Fabian], Schmidt, K.[Kjeld], Bracht, I.[Ingolf], Beecks, C.[Christian],
3CS Algorithm for Efficient Gaussian Process Model Retrieval,
ICPR21(1773-1780)
IEEE DOI 2105
Machine learning algorithms, Heuristic algorithms, Gaussian processes, Data models, Partitioning algorithms, Performance Evaluation BibRef

Carvalho, E.D.C.[Eduardo D. C.], Clark, R.[Ronald], Nicastro, A.[Andrea], Kelly, P.H.J.[Paul H. J.],
Scalable Uncertainty for Computer Vision With Functional Variational Inference,
CVPR20(12000-12010)
IEEE DOI 2008
Uncertainty, Task analysis, Bayes methods, Training, Machine learning, Neural networks BibRef

Kim, T., Lee, J., Choe, Y.,
Tensor Train Decomposition for Efficient Memory Saving in Perceptual Feature-Maps,
RSL-CV19(599-604)
IEEE DOI 2004
approximation theory, Bayes methods, convolutional neural nets, image classification, learning (artificial intelligence), Tensor Train decomposition BibRef

Machireddy, A.[Amrutha], Krishnan, R.[Ranganath], Ahuja, N.[Nilesh], Tickoo, O.[Omesh],
Continual Active Adaptation to Evolving Distributional Shifts,
RoSe22(3443-3449)
IEEE DOI 2210
Adaptation models, Computational modeling, Perturbation methods, Neural networks, Lighting, Predictive models, Data models BibRef

Krishnan, R., Subedar, M., Tickoo, O.,
Efficient Priors for Scalable Variational Inference in Bayesian Deep Neural Networks,
SDL-CV19(773-777)
IEEE DOI 2004
Bayes methods, neural net architecture, statistical distributions, stochastic processes, Bayesian Priors BibRef

Liu, Y.H.[Yu-Hang], Dong, W.Y.[Wen-Yong], Zhang, L.[Lei], Gong, D.[Dong], Shi, Q.F.[Qin-Feng],
Variational Bayesian Dropout With a Hierarchical Prior,
CVPR19(7117-7126).
IEEE DOI 2002
BibRef

Nguyen, K., Le, T., Dinh, T.N., Phung, D.,
Bayesian Multi-Hyperplane Machine for Pattern Recognition,
ICPR18(609-614)
IEEE DOI 1812
Bayes methods, data handling, gradient methods, inference mechanisms, learning (artificial intelligence), Computational modeling BibRef

Pirayre, A., Zheng, Y., Duval, L., Pesquet, J.C.,
HOGMep: Variational Bayes and higher-order graphical models applied to joint image recovery and segmentation,
ICIP17(3775-3779)
IEEE DOI 1803
Bayes methods, Graphical models, Image restoration, Image segmentation, Indexes, Probability density function, variational Bayes BibRef

Dogadov, S., Masegosa, A., Nakajima, S.,
Variational Robust Subspace Clustering with Mean Update Algorithm,
RSL-CV17(1792-1799)
IEEE DOI 1802
Approximation algorithms, Bayes methods, Clustering algorithms, Computational modeling, Dictionaries, Robustness, Sparse matrices BibRef

Fathy, M.E., Chellappa, R.,
Image Set Classification Using Sparse Bayesian Regression,
WACV17(1187-1196)
IEEE DOI 1609
Bayes methods, Computational modeling, Dictionaries, Face recognition, Kernel, Manifolds, Probes BibRef

Vogt, K.[Karsten], Ostermann, J.[Jörn],
Soft Margin Bayes-Point-Machine Classification via Adaptive Direction Sampling,
SCIA17(I: 313-324).
Springer DOI 1706
BibRef

Nguyen, T.T.T.[Thi Thu Thuy], Nguyen, T.T.[Tien Thanh], Pham, X.C.[Xuan Cuong], Liew, A.W.C.[Alan Wee-Chung], Hu, Y.J.[Yong-Jian], Liang, T.C.[Tian-Cai], Li, C.T.[Chang-Tsun],
A Novel Online Bayes Classifier,
DICTA16(1-6)
IEEE DOI 1701
Approximation algorithms BibRef

Kim, Y.D., Jang, T., Han, B., Choi, S.,
Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework,
CVPR16(5318-5326)
IEEE DOI 1612
BibRef

Maeda, T., Yamasaki, T., Aizawa, K.,
Multi-stage object classification featuring confidence analysis of classifier and inclined local Naive Bayes nearest neighbor,
ICIP14(5177-5181)
IEEE DOI 1502
Accuracy BibRef

Endo, T.[Tomomi], Kudo, M.[Mineichi],
Weighted Naïve Bayes Classifiers by Renyi Entropy,
CIARP13(I:149-156).
Springer DOI 1311
BibRef

Cordella, L.P.[Luigi P.], de Stefano, C.[Claudio],
A Weighted Majority Vote Strategy Using Bayesian Networks,
CIAP13(II:219-228).
Springer DOI 1309
BibRef

Turkov, P.[Pavel], Krasotkina, O.[Olga], Mottl, V.[Vadim],
The Bayesian logistic regression in pattern recognition problems under concept drift,
ICPR12(2976-2979).
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BibRef

Aghajanian, J.[Jania], Warrell, J.[Jonathan], Prince, S.J.D.[Simon J.D.], Li, P.[Peng], Rohn, J.L.[Jennifer L.], Baum, B.[Buzz],
Patch-based within-object classification,
ICCV09(1125-1132).
IEEE DOI 0909
E.g. Gender in face, pose in pedestrian. Within-object classification. BibRef

Goswami, D., Kalkan, S., Krüger, N.,
Bayesian Classification of Image Structures,
SCIA09(676-685).
Springer DOI 0906
BibRef

Chandra, B., Gupta, M.[Manish], Gupta, M.P.,
Robust Approach for Estimating Probabilities in Naive-Bayes Classifier,
PReMI07(11-16).
Springer DOI 0712
BibRef

Wang, W.[Wei], Wang, C.H.[Chun-Heng], Cui, X.[Xia], Wang, A.[Ai],
A clustering algorithm combine the FCM algorithm with supervised learning normal mixture model,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Xuan, G.R.[Guo-Rong], Zhu, X.M.[Xiu-Ming], Shi, Y.Q.[Yun Q.], Chai, P.Q.[Pei-Qi], Cui, X.[Xia], Li, J.[Jue],
A Novel Bayesian Classifier with Smaller Eigenvalues Reset by Threshold Based on Given Database,
ICIAR07(375-386).
Springer DOI 0708

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Okatani, T.[Takayuki], Deguchi, K.[Koichiro],
Variational Bayes Based Approach to Robust Subspace Learning,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Yuan, C.[Chao], Neubauer, C.[Claus],
A Variational Bayesian Approach for Classification with Corrupted Inputs,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Phung, S.L.[Son Lam], Bouzerdoum, A., Chai, D., Watson, A.,
Naive bayes face/nonface classifier: a study of preprocessing and feature extraction techniques,
ICIP04(II: 1385-1388).
IEEE DOI 0505
BibRef

Jeon, Y.J.[Young-Joon], Choi, J.G.[Jae-Gark], Kim, J.I.[Jin-Il],
A Study on Supervised Classification of Remote Sensing Satellite Image by Bayesian Algorithm Using Average Fuzzy Intracluster Distance,
IWCIA04(597-606).
Springer DOI 0505
BibRef

Jermyn, I.H.[Ian H.],
On Bayesian Estimation in Manifolds,
INRIARR-4607, Octobre 2002.
HTML Version. 0306
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Mathis, C., Breuel, T.,
Classification using a hierarchical bayesian approach,
ICPR02(IV: 103-106).
IEEE DOI 0211
BibRef

Carvalho, P.C.P.[Paulo C.P.], Santos, A.[Amancio], Dourado, A.[Antonio], Ribeiro, B.[Bernardete],
Bayes information criterion for Tikhonov regularization with linear constraints: application to spectral data estimation,
ICPR02(I: 696-700).
IEEE DOI 0211
BibRef

Keren, D.,
Painter identification using local features and naive bayes,
ICPR02(II: 474-477).
IEEE DOI 0211
BibRef

Qi, Y.[Yuan], Picard, R.W.,
Context-sensitive Bayesian classifiers and application to mouse pressure pattern classification,
ICPR02(III: 448-451).
IEEE DOI 0211
BibRef

Ling, L.L.[Lee Luan], Cavalcanti, H.M.,
Fast and Efficient Feature Extraction Based on Bayesian Decision Boundaries,
ICPR00(Vol II: 390-393).
IEEE DOI 0009
BibRef

Sze, L., Leung, C.,
Branch and Bound Algorithm for the Bayes Classifier,
ICPR96(II: 705-709).
IEEE DOI 9608
(Univ. of Hong Kong, HK) BibRef

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

Last update:Sep 15, 2024 at 16:30:49