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Statistical pattern recognition; Naive Bayes classifier (NB);
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Feature vectors have missing features.
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Bayesian network; Variable elimination; Elimination ordering;
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0803
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Classification; Binary images; Bayesian networks; Variational inference
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0912
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1006
BibRef
Earlier:
ICDAR09(1201-1205).
IEEE DOI
0907
BibRef
Earlier:
Classification and Automatic Annotation Extension of Images Using
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SSPR08(937-946).
Springer DOI
0812
Probabilistic graphical models; Bayesian networks; Image classification; Image annotation; Semantic similarity; Wordnet; Visual features; Bayesian classifier
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Image Annotation Using a Semantic Hierarchy,
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1810
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Earlier:
Images Annotation Extension Based on User Feedback,
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Automatic annotation extension and classification of documents using
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ICDAR15(316-320)
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1511
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Earlier: A1, A3, A2:
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CAIP15(II:579-590).
Springer DOI
1511
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Eches, O.,
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Bayesian Estimation of Linear Mixtures Using the Normal Compositional
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1006
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Eches, O.,
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1112
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Uezato, T.,
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1807
geophysical image processing, geophysical signal processing,
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1101
Dirichlet distribution; Generalized Dirichlet distribution; Naive
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Automatic relevance detection; Empirical Bayes; LASSO; Sparse model;
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1209
BibRef
And:
Optimal 'anti-bayesian' Parametric Pattern Classification Using Order
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CIARP12(1-13).
Springer DOI
1209
BibRef
Earlier:
Optimal 'Anti-Bayesian' Parametric Pattern Classification for the
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ICIAR12(I: 11-18).
Springer DOI
1206
Pattern classification; Order statistics; Reduction of training
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BibRef
Oommen, B.J.[B. John],
Thomas, A.,
'Anti-Bayesian' parametric pattern classification using order
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Elsevier DOI
1310
Pattern classification
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Thomas, A.[Anu],
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On Achieving Near-Optimal 'Anti-Bayesian' Order Statistics-Based
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CAIP13(368-376).
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1308
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Thomas, A.[Anu],
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Corrigendum to three papers that deal with 'Anti'-Bayesian Pattern
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1403
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Thomas, A.[Anu],
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1308
Classification using Order Statistics (OS)
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Hammer, H.L.[Hugo Lewi],
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1801
Anti-Bayesian classification
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1304
Latent Dirichlet allocation-hierarchical Bayesian model.
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1403
Bayesian networks
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Carvalho, A.M.[Alexandra M.],
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1406
Conditional log-likelihood
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Liu, Y.,
Simeone, O.,
Haimovich, A.M.,
Su, W.,
Modulation Classification via Gibbs Sampling Based on a Latent
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IEEE DOI
1406
Bayes methods
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Filippone, M.,
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1410
Approximation methods
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Filippone, M.[Maurizio],
Bayesian Inference for Gaussian Process Classifiers with Annealing
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ICPR14(614-619)
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1412
Annealing
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Ortega, P.A.[Pedro A.],
Subjectivity, Bayesianism, and causality,
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Elsevier DOI
1509
Subjectivity
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Duan, H.P.[Hui-Ping],
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Pattern-Coupled Sparse Bayesian Learning for Inverse Synthetic
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IEEE DOI
1509
Gaussian processes
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Fang, J.[Jun],
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Two-Dimensional Pattern-Coupled Sparse Bayesian Learning via
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1605
Bayes methods
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Bouguelia, M.R.[Mohamed-Rafik],
Belaïd, Y.[Yolande],
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PRL(70), No. 1, 2016, pp. 38-44.
Elsevier DOI
1602
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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],
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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:
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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
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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,
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Nautsch, A.,
Meuwly, D.,
Ramos, D.,
Lindh, J.,
Busch, C.,
Making Likelihood Ratios Digestible for Cross-Application Performance
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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
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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
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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,
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Online learning/processing, Variational methods, Bayes procedures
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Bayes methods, Dimensionality reduction, Geometry,
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Marghi, Y.M.[Yeganeh M.],
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Elsevier DOI
2202
Active learning, Recursive state estimation,
Bayesian inference, Rényi entropy
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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,
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Yu, H.[Hang],
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2212
Graphical models, Covariance matrices, Time series analysis,
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Classification, Parameter learning, Sample size,
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2311
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2403
Data discretization, Maximal dependency, Maximal relevance,
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Plug-and-Play Split Gibbs Sampler:
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IEEE DOI
2406
Noise reduction, Stochastic processes, Inverse problems,
Data models, Bayes methods, Task analysis, Kernel, inverse problem
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Geometry-Aware Hierarchical Bayesian Learning on Manifolds,
WACV22(2743-2752)
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2202
Manifolds, Representation learning, Point cloud compression,
Convolution, Vision for Graphics
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ICPR21(9635-9642)
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2105
Support vector machines, Itemsets, Computational modeling,
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ICPR21(10297-10304)
IEEE DOI
2105
Training, Deep learning, Quantization (signal), Neural networks,
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Wang, H.[Hu],
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Uncertainty in Model-Agnostic Meta-Learning using Variational
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WACV20(3079-3089)
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2006
Task analysis, Training, Bayes methods, Computational modeling,
Uncertainty, Machine learning, Adaptation models
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Wang, Q.L.[Qi-Long],
Li, P.H.[Pei-Hua],
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Cheng, Z.[Zezhou],
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Stochastic Normalizations as Bayesian Learning,
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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
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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
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Gao, X.G.[Xiao-Guang],
Yang, Y.[Yu],
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Bayesian approach to learn Bayesian networks using data and
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ICPR16(3667-3672)
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1705
Bayes methods, Estimation, Heuristic algorithms,
Learning systems, Mathematical model, Parameter, estimation
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Nie, S.Q.[Si-Qi],
Zhao, Y.[Yue],
Ji, Q.A.[Qi-Ang],
Latent regression Bayesian network for data representation,
ICPR16(3494-3499)
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1705
Approximation algorithms, Bayes methods, Computational modeling,
Data models, Inference algorithms, Linear, programming
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Li, F.Y.,
Shafiee, M.J.,
Chung, A.G.,
Chwyl, B.,
Kazemzadeh, F.,
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High dynamic range map estimation via fully connected random fields
with stochastic cliques,
ICIP15(2159-2163)
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1512
Conditional Random Fields
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Shafiee, M.J.,
Siva, P.,
Fieguth, P.W.[Paul W.],
Wong, A.,
Efficient Deep Feature Learning and Extraction via StochasticNets,
Robust16(1101-1109)
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1612
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Chung, A.G.,
Shafiee, M.J.,
Wong, A.,
Image Restoration via Deep-Structured Stochastically Fully-Connected
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CRV16(194-200)
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1612
CRF
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Shafiee, M.J.,
Chung, A.G.,
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Fieguth, P.W.,
Improved fine structure modeling via guided stochastic clique
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1512
CRF
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Shafiee, M.J.,
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Efficient Bayesian inference using fully connected conditional random
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ICIP14(4289-4293)
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1502
Computational modeling
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Terzic, K.[Kasim],
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An efficient Naive Bayes approach to category-level object detection,
ICIP14(1658-1662)
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Complexity theory
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1407
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Unsupervised feature learning using Markov deep belief network,
ICIP13(260-264)
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1402
Computational modeling
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1209
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A Variational Bayesian EM Algorithm for Tree Similarity,
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1008
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Learning Bayesian Networks with qualitative constraints,
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0806
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Gomes, R.[Ryan],
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Perona, P.[Pietro],
Incremental learning of nonparametric Bayesian mixture models,
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0806
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Bayesian Feedback in Data Clustering,
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0609
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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.
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Adaptive Bayesian networks for video processing,
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0312
Adapt, or learn, while processing.
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Fergus, R.[Rob],
Perona, P.[Pietro],
Zisserman, A.[Andrew],
A Sparse Object Category Model for Efficient Learning and Complete
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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
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DAS02(134 ff.).
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0303
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Souafi-Bensafi, S.,
Parizeau, M.,
Le Bourgeois, F.,
Emptoz, H.,
Bayesian networks classifiers applied to documents,
ICPR02(I: 483-486).
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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).
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0211
BibRef
Vailaya, A.,
Jain, A.K.,
Reject Option for VQ-based Bayesian Classification,
ICPR00(Vol II: 48-51).
IEEE DOI
0009
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Vailaya, A.[Aditya],
Jain, A.K.[Anil K.],
Incremental Learning for Bayesian Classification of Images,
ICIP99(II:585-589).
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BibRef
9900
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Genetic Algorithms, Genetic Programming .