14.5 Learning in Computer Vision

Torch: Machine-Learning Library,
2004.
WWW Link. Code, Learning. Open source learning library.

Underwood, S.A., and Coates, C.L.,
Visual Learning from Multiple Views,
TC(24), No. 6, June 1975, pp. 651-661. An old basically straightforward method for matching and learning. BibRef 7506

Sherman, R., Ernst, G.W.,
Learning patterns in terms of other patterns,
PR(1), No. 4, July 1969, pp. 301-313.
Elsevier DOI 0309
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Shimura, M.[Masamichi],
Recognizing machines with parametric and nonparametric learning methods using contextual information,
PR(5), No. 2, June 1973, pp. 149-168.
Elsevier DOI 0309
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Imai, T.[Toshio], Shimura, M.[Masamichi],
Learning with probabilistic labeling,
PR(8), No. 1, January 1976, pp. 5-10.
Elsevier DOI 0309
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Stonham, T.J., Shaw, M.A.,
Automatic Classification of Mass Spectra by Means of Digital Learning Nets: Existence of Characteristic Features of Chemical Class in Mass Spectra,
PR(7), No. 4, December 1975, pp. 235-241.
Elsevier DOI 0309
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Williams, H.[Harold],
A net-structure learning system for pattern description,
PR(8), No. 4, October 1976, pp. 261-271.
Elsevier DOI 0309
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Coulon, D.[Daniel], Kayser, D.[Daniel],
Learning criterion and inductive behaviour,
PR(10), No. 1, 1978, pp. 19-25.
Elsevier DOI 0309
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Gowda, K.C.[K. Chidananda], Krishna, G.,
Learning with a mutualistic teacher,
PR(11), No. 5-6, 1979, pp. 383-390.
Elsevier DOI 0309
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Takiyama, R.[Ryuzo],
A learning procedure for multisurface method of pattern separation,
PR(12), No. 2, 1980, pp. 75-82.
Elsevier DOI 0309
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Greblicki, W.[Wlodzimierz],
Learning to recognize patterns with a probabilistic teacher,
PR(12), No. 3, 1980, pp. 159-164.
Elsevier DOI 0309
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Chittineni, C.B.,
Learning with imperfectly labeled patterns,
PR(12), No. 5, 1980, pp. 281-291.
Elsevier DOI 0309
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Krishnan, T.,
Efficiency of learning with imperfect supervision,
PR(21), No. 2, 1988, pp. 183-188.
Elsevier DOI 0309
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Bergadano, F., Giordana, A., Saitta, L.,
Automated concept acquisition in noisy environments,
PAMI(10), No. 4, July 1988, pp. 555-578.
IEEE DOI 0401
BibRef

Baim, P.W.,
A method for attribute selection in inductive learning systems,
PAMI(10), No. 6, November 1988, pp. 888-896.
IEEE DOI 0401
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Kodratoff, Y., Tecuci, G.,
Learning based on conceptual distance,
PAMI(10), No. 6, November 1988, pp. 897-909.
IEEE DOI 0401
BibRef

Constant, P., Matwin, S., Oppacher, F.,
LEW: learning by watching,
PAMI(12), No. 3, March 1990, pp. 294-308.
IEEE DOI 0401
BibRef

Chitoor, S.S.[Suresh S.], Murty, M.N.[M. Narasimha], Bhandaru, M.K.[Malini K.],
A new data structure HC-expression for learning from examples,
PR(24), No. 1, 1991, pp. 19-29.
Elsevier DOI 0401
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Bhandaru, M.K.[Malini K.], Murty, M.N.[M. Narasimha],
Incremental learning from examples using HC-expressions,
PR(24), No. 4, 1991, pp. 273-282.
Elsevier DOI 0401
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Lugosi, G.[Gábor],
Learning with an unreliable teacher,
PR(25), No. 1, January 1992, pp. 79-87.
Elsevier DOI 0401
BibRef

Saitta, L., Bergadano, F.,
Pattern recognition and Valiant's learning framework,
PAMI(15), No. 2, February 1993, pp. 145-155.
IEEE DOI 0401
BibRef

Cho, K., Dunn, S.M.,
Learning Shape Classes,
PAMI(16), No. 9, September 1994, pp. 882-888.
IEEE DOI BibRef 9409
Earlier: MDSG94(483-492). BibRef
Earlier:
Shape-based object recognition by inductive learning,
ICPR92(II:681-684).
IEEE DOI 9208
BibRef

Chen, L.H.[Liang-Hwa], Chang, S.[Shyang],
An adaptive conscientious competitive learning algorithm and its applications,
PR(27), No. 12, December 1994, pp. 1787-1813.
Elsevier DOI 0401
BibRef

Ching, J.Y., Wong, A.K.C., Chan, K.C.C.,
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data,
PAMI(17), No. 7, July 1995, pp. 641-651.
IEEE DOI BibRef 9507

Park, J.M., Hu, Y.H.,
Online Learning for Active Pattern-Recognition,
SPLetters(3), No. 11, November 1996, pp. 301-303.
IEEE Top Reference. 9611
BibRef

Kim, J., Yu, J.R., Kim, S.H.,
Learning of Prototypes and Decision Boundaries for a Verification Problem Having Only Positive Samples,
PRL(17), No. 7, June 10 1996, pp. 691-697. 9607
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Cervera, E., Delpobil, A.P., Marta, E., Serna, M.A.,
Perception-Based Learning for Motion in Contact in Task Planning,
JIRS(17), No. 3, November 1996, pp. 283-308. 9701
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Dasarathy, B.V., and Lakshminarasimhan, A.L.,
Learning Under a VEDIC Teacher,
CIS(8), March 1979, No. 1, pp. 75-88. BibRef 7903

Dasarathy, B.V.,
Nosing Around the Neighborhood: A New System Structure and Classification Rule for Recognition in Partially Exposed Environments,
PAMI(2), No. 1, January 1980, pp. 67-71. BibRef 8001

Dasarathy, B.V.,
Adaptive Decision Systems with Extended Learning for Deployment in Partially Exposed Environments,
OptEng(34), No. 5, May 1995, pp. 1269-1280. BibRef 9505
And:
Adaptive Learning Concepts and Methodology for Enhanced Recognition System Performance,
SPIE(2234), Automatic Object Recognition IV, July 1994, pp. 372-383. BibRef
And:
Feature Assessment in Imperfectly Supervised Environment,
SPIE(2234), July 1994, pp. 360-371. BibRef

Dasarathy, B.V.,
Fuzzy Learning in Vicissitudinous Environments,
ICPR92(II:500-503).
IEEE DOI BibRef 9200
And:
FLUTE: Fuzzy Learning In Unfamiliar Teacher Environments,
IEEE_Fuzzy Systems 92(1070-1077), March 1992. BibRef

Kors, J.A., Hoffmann, A.L.,
Induction of Decision Rules That Fulfill User Specified Performance Requirements,
PRL(18), No. 11-13, November 1997, pp. 1187-1195. 9806
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Shah, J.[Jayant],
Reaction-Diffusion Equations and Learning,
JVCIR(13), No. 1/2, March/June 2002, pp. 82-93.
DOI Link 0204
BibRef

Shah, J.[Jayant],
Minimax Entropy and Learning by Diffusion,
CVPR98(92-97).
IEEE DOI BibRef 9800

Wyeth, G.,
Training A Vision-Guided Mobile Robot,
MachLearn(31), No. 1-3, Apr-Jun 1998, pp. 201-222. 9809
BibRef

Yokomor, T., Kobayashi, S.,
Learning Local Languages and Their Application to DNA Sequence Analysis,
PAMI(20), No. 10, October 1998, pp. 1067-1079.
IEEE DOI BibRef 9810

Wang, H.[Hui], Bell, D.[David], Murtagh, F.[Fionn],
Axiomatic Approach to Feature Subset Selection Based on Relevance,
PAMI(21), No. 3, March 1999, pp. 271-277.
IEEE DOI Machine Learning. BibRef 9903

Ratsaby, J.,
Incremental Learning with Sample Queries,
PAMI(20), No. 8, August 1998, pp. 883-888.
IEEE DOI BibRef 9808

Beymer, D.J.[David J.], and Poggio, T.[Tomaso],
Image Representations for Visual Learning,
Science(272), No. 5250, June 28, 1996, pp. 1905.
PS File. BibRef 9606

Inoue, K.[Kohei], Urahama, K.[Kiichi],
Learning of view-invariant pattern recognizer with temporal context,
PR(33), No. 10, October 2000, pp. 1665-1674.
Elsevier DOI 0006
BibRef

Inoue, K.[Kohei], Urahama, K.[Kiichi],
Equivalence of Non-Iterative Algorithms for Simultaneous Low Rank Approximations of Matrices,
CVPR06(I: 154-159).
IEEE DOI 0606
BibRef

Kim, W.S., Cho, H.S.,
Learning-based constitutive parameters estimation in an image sensing system with multiple mirrors,
PR(33), No. 7, July 2000, pp. 1199-1217.
Elsevier DOI 0005
BibRef

Freeman, W.T.[William T.], Pasztor, E.C.[Egon C.], Carmichael, O.T.[Owen T.],
Learning Low-Level Vision,
IJCV(40), No. 1, October 2000, pp. 25-47.
DOI Link 0101
BibRef
Earlier: A1 and A2 only: ICCV99(1182-1189).
IEEE DOI And:
WWW Link. BibRef

Imiya, A.[Atsushi], Kawamoto, K.[Kazuhiko],
Learning dimensionality and orientations of 3D objects,
PRL(22), No. 1, January 2001, pp. 75-83.
Elsevier DOI 0105
BibRef

Oommen, B.J., Agache, M.,
Continuous and Discretized Pursuit Learning Schemes: Various Algorithms and Their Comparison,
SMC-B(31), No. 3, June 2001, pp. 277-287.
IEEE Top Reference. 0108
BibRef

Bonarini, A., Bonacina, C., Matteucci, M.,
An approach to the design of reinforcement functions in real world, agent-based applications,
SMC-B(31), No. 3, June 2001, pp. 288-301.
IEEE Top Reference. 0108
BibRef

Lam, W.[Wai], Keung, C.K.[Chi-Kin], Ling, C.X.[Charles X.],
Learning good prototypes for classification using filtering and abstraction of instances,
PR(35), No. 7, July 2002, pp. 1491-1506.
Elsevier DOI 0204
BibRef

Freedman, D.[Daniel],
Efficient Simplicial Reconstructions of Manifolds from Their Samples,
PAMI(24), No. 10, October 2002, pp. 1349-1357.
IEEE Abstract. 0210
BibRef

Kisilev, P.[Pavel], Freedman, D.[Daniel],
Parameter Tuning by Pairwise Preferences,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Chawla, N.V.[Nitesh V.], Moore, T.E.[Thomas E.], Hall, L.O.[Lawrence O.], Bowyer, K.W.[Kevin W.], Kegelmeyer, W.P.[W. Philip], Springer, C.[Clayton],
Distributed learning with bagging-like performance,
PRL(24), No. 1-3, January 2003, pp. 455-471.
Elsevier DOI 0211
BibRef

Granger, E.[Eric], Savaria, Y.[Yvon], Lavoie, P.[Pierre],
A pattern reordering approach based on ambiguity detection for online category learning,
PAMI(25), No. 4, April 2003, pp. 525-529.
IEEE Abstract. 0304
BibRef

Al-Shaher, A.A.[Abdullah A.], Hancock, E.R.[Edwin R.],
Learning mixtures of point distribution models with the EM algorithm,
PR(36), No. 12, December 2003, pp. 2805-2818.
Elsevier DOI 0310
BibRef
Earlier:
Fast on-line learning of point distribution models,
ICPR02(II: 208-211).
IEEE DOI 0211
BibRef

Seow, M.J.[Ming-Jung], Asari, V.K.[Vijayan K.],
Learning using distance based training algorithm for pattern recognition,
PRL(25), No. 2, January 2004, pp. 189-196.
Elsevier DOI 0401
BibRef

Cherkassky, V.S.[Vladimir S.], Shao, X., Mulier, F., Vapnik, V.,
Model Selection for Regression Using VC-Generalization Bounds,
TNN(10), 1999, pp. 1075-1089. BibRef 9900

Park, J.M.[Jong-Min],
Convergence and Application of Online Active Sampling Using Orthogonal Pillar Vectors,
PAMI(26), No. 9, September 2004, pp. 1197-1207.
IEEE Abstract. 0409
Sample at the boundary approach. BibRef

Lopes, M.C., Santos-Victor, J.,
Visual Learning by Imitation With Motor Representations,
SMC-B(35), No. 3, June 2005, pp. 438-449.
IEEE DOI 0508
BibRef

Anastasiadis, A.D., Magoulas, G.D., Vrahatis, M.N.[Michael N.],
Sign-based learning schemes for pattern classification,
PRL(26), No. 12, September 2005, pp. 1926-1936.
Elsevier DOI 0508
BibRef

Ikonomakis, E.K.[Emmanouil K.], Spyrou, G.M.[George M.], Vrahatis, M.N.[Michael N.],
Content driven clustering algorithm combining density and distance functions,
PR(87), 2019, pp. 190-202.
Elsevier DOI 1812
Clustering algorithms, Density based clustering, Distance based clustering, Evolutionary clustering, Window density function BibRef

Alissandrakis, A.[Aris], Nehaniv, C.L.[Chrystopher L.], Dautenhahn, K.[Kerstin],
Correspondence Mapping Induced State and Action Metrics for Robotic Imitation,
SMC-B(37), No. 2, April 2007, pp. 299-307.
IEEE DOI 0704
BibRef

Lopes, M.[Manuel], Santos-Victor, J.[Jose],
A Developmental Roadmap for Learning by Imitation in Robots,
SMC-B(37), No. 2, April 2007, pp. 308-321.
IEEE DOI 0704
BibRef

Pardowitz, M.[Michael], Knoop, S.[Steffen], Dillmann, R.[Ruediger], Zollner, R.D.[Raoul D.],
Incremental Learning of Tasks From User Demonstrations, Past Experiences, and Vocal Comments,
SMC-B(37), No. 2, April 2007, pp. 322-332.
IEEE DOI 0704
BibRef

Girgin, S., Polat, F., Alhajj, R.,
Positive Impact of State Similarity on Reinforcement Learning Performance,
SMC-B(37), No. 5, October 2007, pp. 1256-1270.
IEEE DOI 0711
BibRef

Sage, K.[Kingsley], Howell, A.J.[A. Jonathan], Buxton, H.[Hilary], Argyros, A.A.[Antonis A.],
Learning temporal structure for task based control,
IVC(26), No. 1, 1 January 2008, pp. 39-52.
Elsevier DOI 0711
Variable length Markov models; Temporal learning; 3-D Tracking; Data association; Task-based control BibRef

Sage, K., Buxton, H.,
Joint spatial and temporal structure learning for task based control,
ICPR04(II: 48-51).
IEEE DOI 0409
BibRef

Haro, G.[Gloria], Randall, G.[Gregory], Sapiro, G.[Guillermo],
Translated Poisson Mixture Model for Stratification Learning,
IJCV(80), No. 3, December 2008, pp. xx-yy.
Springer DOI 0810
BibRef
Earlier:
Regularized Mixed Dimensionality and Density Learning in Computer Vision,
ComponentAnalysis07(1-8).
IEEE DOI 0706
A framework for the regularized and robust estimation of non-uniform dimensionality and density in high dimensional noisy data. BibRef

Xia, F.[Fen], Yang, Y.W.[Yan-Wu], Zhou, L.[Liang], Li, F.X.[Fu-Xin], Cai, M.[Min], Zeng, D.D.[Daniel D.],
A closed-form reduction of multi-class cost-sensitive learning to weighted multi-class learning,
PR(42), No. 7, July 2009, pp. 1572-1581.
Elsevier DOI 0903
Cost-sensitive learning; Supervised learning; Statistical learning theory; Classification BibRef

Felsberg, M.[Michael], Wiklund, J.[Johan], Granlund, G.H.[Gosta H.],
Exploratory learning structures in artificial cognitive systems,
IVC(27), No. 11, 2 October 2009, pp. 1671-1687.
Elsevier DOI 0909
Cognitive systems; COSPAL; Perception-action learning BibRef

Ong, E.J.[Eng-Jon], Ellis, L.[Liam], Bowden, R.[Richard],
Problem solving through imitation,
IVC(27), No. 11, 2 October 2009, pp. 1715-1728.
Elsevier DOI 0909
Cognitive system; Complexity Chain; Learning from imitation; Problem solving BibRef

Larsson, F.[Fredrik], Jonsson, E.[Erik], Felsberg, M.[Michael],
Simultaneously learning to recognize and control a low-cost robotic arm,
IVC(27), No. 11, 2 October 2009, pp. 1729-1739.
Elsevier DOI 0909
Visual servoing; LWPR; Gripper recognition; Jacobian estimation BibRef

Jin, X.B.[Xiao-Bo], Liu, C.L.[Cheng-Lin], Hou, X.W.[Xin-Wen],
Regularized margin-based conditional log-likelihood loss for prototype learning,
PR(43), No. 7, July 2010, pp. 2428-2438.
Elsevier DOI 1003
BibRef
Earlier:
Prototype learning with margin-based conditional log-likelihood loss,
ICPR08(1-4).
IEEE DOI 0812
Prototype learning; Conditional log-likelihood loss; Log-likelihood of margin (LOGM); Regularization; Distance metric learning BibRef

Zheng, N.N.[Nan-Ning], Xue, J.R.[Jian-Ru],
Statistical Learning and Pattern Analysis for Image and Video Processing,
Springer-Verlag2009. ISBN: 978-1-84882-311-2
WWW Link. 0104
Buy this book: Statistical Learning and Pattern Analysis for Image and Video Processing (Advances in Pattern Recognition) Learning and applications in video coding and processing. BibRef

Wang, F.[Fei],
A general learning framework using local and global regularization,
PR(43), No. 9, September 2010, pp. 3120-3129.
Elsevier DOI 1006
Machine learning; Local; Global; Regularization BibRef

Pavlidis, N.G., Tasoulis, D.K., Adams, N.M., Hand, D.J.,
lambda-Perceptron: An adaptive classifier for data streams,
PR(44), No. 1, January 2011, pp. 78-96.
Elsevier DOI 1003
Streaming data; Classification; Population drift; Online learning; Forgetting BibRef

Ross, G.J.[Gordon J.], Adams, N.M.[Niall M.], Tasoulis, D.K.[Dimitris K.], Hand, D.J.[David J.],
Exponentially weighted moving average charts for detecting concept drift,
PRL(33), No. 2, 15 January 2012, pp. 191-198.
Elsevier DOI 1112
BibRef
And: Erratum: PRL(33), No. 16, 1 December 2012, pp. 2261.
Elsevier DOI 1210
Streaming classification; Concept drift; Change detection BibRef

Rossi, F.[Fabrice], Villa-Vialaneix, N.[Nathalie],
Consistency of functional learning methods based on derivatives,
PRL(32), No. 8, 1 June 2011, pp. 1197-1209.
Elsevier DOI 1101
Functional Data Analysis; Consistency; Statistical learning; Derivatives; SVM; Smoothing splines; RKHS BibRef

Xu, H.[Huan], Caramanis, C.[Constantine], Mannor, S.[Shie],
Sparse Algorithms Are Not Stable: A No-Free-Lunch Theorem,
PAMI(34), No. 1, January 2012, pp. 187-193.
IEEE DOI 1112
Learning requires sparsity and stability, but these are often incompatible. L1-regularized regression is not stable, L2-regularized regression is stable and not sparse. BibRef

Schaum, A.P.,
CFAR fusion: A replacement for the generalized likelihood ratio test for Neyman-Pearson problems,
AIPR11(1-6).
IEEE DOI 1204
solving composite hypothesis testing problems BibRef

Wei, L.S.[Long-Sheng], Sang, N.[Nong], Wang, Y.H.[Yue-Huan],
A biologically inspired object-based visual attention model,
AIR(34), No. 2, August 2010, pp. 109-119.
WWW Link. 1208
BibRef
Earlier: A2, A1, A3:
A Biologically-Inspired Top-Down Learning Model Based on Visual Attention,
ICPR10(3736-3739).
IEEE DOI 1008
BibRef

Chakraborty, S.[Shayok], Balasubramanian, V.[Vineeth], Panchanathan, S.[Sethuraman],
Generalized batch mode active learning for face-based biometric recognition,
PR(46), No. 2, February 2013, pp. 497-508.
Elsevier DOI 1210
BibRef
Earlier:
Dynamic batch mode active learning,
CVPR11(2649-2656).
IEEE DOI 1106
BibRef
Earlier:
Learning from summaries of videos: Applying batch mode active learning to face-based biometrics,
Biometrics10(130-137).
IEEE DOI 1006
BibRef
Earlier: A2, A1, A3:
Generalized Query by Transduction for online active learning,
Learning09(1378-1385).
IEEE DOI 0910
Active learning; Face-based biometrics; Optimization Predict class label of a point. BibRef

Chakraborty, S.[Shayok], Balasubramanian, V.[Vineeth], Sun, Q., Panchanathan, S.[Sethuraman], Ye, J.,
Active Batch Selection via Convex Relaxations with Guaranteed Solution Bounds,
PAMI(37), No. 10, October 2015, pp. 1945-1958.
IEEE DOI 1509
BibRef

Chakraborty, S.[Shayok],
Distributed Active Learning for Image Recognition,
WACV18(1833-1841)
IEEE DOI 1806
distributed processing, image recognition, learning (artificial intelligence), active learning algorithms, Uncertainty BibRef

Young, S.R., Davis, A., Mishtal, A., Arel, I.,
Hierarchical spatiotemporal feature extraction using recurrent online clustering,
PRL(37), No. 1, 2014, pp. 115-123.
Elsevier DOI 1402
Deep machine learning. BibRef

Ataer-Cansizoglu, E.[Esra], Akcakaya, M.[Murat], Orhan, U.[Umut], Erdogmus, D.[Deniz],
Manifold learning by preserving distance orders,
PRL(38), No. 1, 2014, pp. 120-131.
Elsevier DOI 1402
Machine learning BibRef

Ataer-Cansizoglu, E.[Esra], Akcakaya, M.[Murat], Erdogmus, D.,
Minor Surfaces are Boundaries of Mode-Based Clusters,
SPLetters(22), No. 7, July 2015, pp. 891-895.
IEEE DOI 1412
Clustering algorithms BibRef

Ji, N.N.[Nan-Nan], Zhang, J.S.[Jiang-She], Zhang, C.X.[Chun-Xia],
A sparse-response deep belief network based on rate distortion theory,
PR(47), No. 9, 2014, pp. 3179-3191.
Elsevier DOI 1406
Deep belief network BibRef

Zhang, H.[He], Yang, Z.R.[Zhi-Rong], Oja, E.[Erkki],
Improving cluster analysis by co-initializations,
PRL(45), No. 1, 2014, pp. 71-77.
Elsevier DOI 1407
Clustering BibRef

Zhu, Z.X.[Zhan-Xing], Yang, Z.R.[Zhi-Rong], Oja, E.[Erkki],
Multiplicative Updates for Learning with Stochastic Matrices,
SCIA13(143-152).
Springer DOI 1311
BibRef

Xu, C.[Chang], Tao, D.C.[Da-Cheng], Xu, C.[Chao],
Large-Margin Multi-View Information Bottleneck,
PAMI(36), No. 8, August 2014, pp. 1559-1572.
IEEE DOI 1407
Accuracy learning from examples represented by multi-view features. BibRef

Xu, C.[Chang], Tao, D.C.[Da-Cheng], Xu, C.[Chao],
Multi-View Intact Space Learning,
PAMI(37), No. 12, December 2015, pp. 2531-2544.
IEEE DOI 1512
image reconstruction BibRef

Persello, C., Boularias, A., Dalponte, M., Gobakken, T., Naesset, E., Scholkopf, B.,
Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification,
GeoRS(52), No. 10, October 2014, pp. 6652-6664.
IEEE DOI 1407
Accuracy BibRef

Nowozin, S.[Sebastian], Lampert, C.H.[Christoph H.],
Structured Learning and Prediction in Computer Vision,
FTCGV(6), Issue 3-4, 2010, pp. 185-365.
DOI Link 1410
Published May 2011. BibRef

Gu, Y.J.[Ying-Jie], Jin, Z.[Zhong], Chiu, S.C.,
Active learning combining uncertainty and diversity for multi-class image classification,
IET-CV(9), No. 3, 2015, pp. 400-407.
DOI Link 1507
computer vision BibRef

Zhu, Y.J.[Yu-Jin], Wang, Z.[Zhe], Gao, D.Q.[Da-Qi],
Matrixized learning machine with modified pairwise constraints,
PR(48), No. 11, 2015, pp. 3797-3809.
Elsevier DOI 1506
Matrixized classifier BibRef

Tang, Y., Yuan, Y.,
Image Pair Analysis With Matrix-Value Operator,
Cyber(45), No. 10, October 2015, pp. 2042-2050.
IEEE DOI 1509
Algorithm design and analysis BibRef

Zahalka, J., Rudinac, S., Worring, M.,
Interactive Multimodal Learning for Venue Recommendation,
MultMed(17), No. 12, December 2015, pp. 2235-2244.
IEEE DOI 1512
Cities and towns BibRef

Zhou, Z.Z.[Zhao-Ze], Zheng, W.S.[Wei-Shi], Hu, J.F.[Jian-Fang], Xu, Y.[Yong], You, J.[Jane],
One-pass online learning: A local approach,
PR(51), No. 1, 2016, pp. 346-357.
Elsevier DOI 1601
One-pass online learning BibRef

Marée, R.[Raphaël], Geurts, P.[Pierre], Wehenkel, L.[Louis],
Towards generic image classification using tree-based learning: An extensive empirical study,
PRL(74), No. 1, 2016, pp. 17-23.
Elsevier DOI 1604
Image classification BibRef

Takigawa, I.[Ichigaku], Mamitsuka, H.[Hiroshi],
Generalized Sparse Learning of Linear Models Over the Complete Subgraph Feature Set,
PAMI(39), No. 3, March 2017, pp. 617-624.
IEEE DOI 1702
Convergence BibRef

Nguyen, C.H.[Canh Hao], Mamitsuka, H.[Hiroshi],
Learning on Hypergraphs With Sparsity,
PAMI(43), No. 8, August 2021, pp. 2710-2722.
IEEE DOI 2107
Noise measurement, Data models, Laplace equations, Additives, Machine learning, Computational modeling, Topology, sparsistency BibRef

Lv, F.M.[Feng-Mao], Yang, G.W.[Guo-Wu], Zhu, W.[William], Liu, C.[Chuan],
Generative classification model for categorical data based on latent Gaussian process,
PRL(92), No. 1, 2017, pp. 56-61.
Elsevier DOI 1705
Machine learning BibRef

Li, Q.[Qin], Shi, X.S.[Xiao-Shuang], Zhou, L.F.[Lin-Fei], Bao, Z.F.[Zhi-Feng], Guo, Z.H.[Zhen-Hua],
Active learning via local structure reconstruction,
PRL(92), No. 1, 2017, pp. 81-88.
Elsevier DOI 1705
Active learning BibRef

Wen, Z., Hou, B., Jiao, L.,
Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification,
IP(26), No. 7, July 2017, pp. 3449-3462.
IEEE DOI 1706
Adaptation models, Analytical models, Computational modeling, Dictionaries, Feature extraction, Testing, Training, Nonlinear analysis cosparse model, analysis operator learning, dictionary learning, discriminative model, generative model, image classification, linear synthesis model, regularization, learning BibRef

Chang, C.C.[Chin-Chun], Liao, B.H.[Bo-Han],
Active learning based on minimization of the expected path-length of random walks on the learned manifold structure,
PR(71), No. 1, 2017, pp. 337-348.
Elsevier DOI 1707
Active, learning BibRef

Zhan, Y.S.[Yu-Sen], Ammar, H.B.[Haitham Bou], Taylor, M.E.[Matthew E.],
Scalable lifelong reinforcement learning,
PR(72), No. 1, 2017, pp. 407-418.
Elsevier DOI 1708
Reinforcement, learning BibRef

Nebot, À.[Àngela], Binefa, X.[Xavier], de Mántaras, R.L.[Ramón López],
Special issue MLAAI: Machine learning and applications in artificial intelligence,
PRL(105), 2018, pp. 1-3.
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Machine learning for big visual analysis,
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One-Pass Learning with Incremental and Decremental Features,
PAMI(40), No. 11, November 2018, pp. 2776-2792.
IEEE DOI 1810
Features are changeing over time. Sensors, Training, Games, Learning systems, Monitoring, Optimization, One-pass learning, incremental and decremental features, robust learning BibRef

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Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification,
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Active Learning with n-ary Queries for Image Recognition,
WACV19(800-808)
IEEE DOI 1904
computational complexity, image annotation, image classification, image recognition, image sampling, integer programming, Machine learning BibRef

Zhao, Y.[Yu], Shi, Z.H.[Zhen-Hui], Zhang, J.Y.[Jing-Yang], Chen, D.[Dong], Gu, L.[Lixu],
A novel active learning framework for classification: Using weighted rank aggregation to achieve multiple query criteria,
PR(93), 2019, pp. 581-602.
Elsevier DOI 1906
Multiple query criteria active learning, Integration criteria strategy, Sample query criterion, Weighted rank aggregation BibRef

Vieting, P.M., de Lamare, R.C., Martin, L., Dartmann, G., Schmeink, A.,
Likelihood-Based Adaptive Learning in Stochastic State-Based Models,
SPLetters(26), No. 7, July 2019, pp. 1031-1035.
IEEE DOI 1906
Adaptation models, Stochastic processes, Convergence, Adaptive learning, Biological system modeling, Reliability, adaptive signal processing BibRef

Danaee, A., de Lamare, R.C., Nascimento, V.H.,
Energy-Efficient Distributed Learning With Coarsely Quantized Signals,
SPLetters(28), 2021, pp. 329-333.
IEEE DOI 2102
Signal processing algorithms, Quantization (signal), Power demand, Peer-to-peer computing, Internet of Things, coarse quantization BibRef

Cao, H.[Hung], Wachowicz, M.[Monica],
A Holistic Overview of Anticipatory Learning for the Internet of Moving Things: Research Challenges and Opportunities,
IJGI(9), No. 4, 2020, pp. xx-yy.
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Vlaski, S.[Stefan], Rizk, E.[Elsa], Sayed, A.H.[Ali H.],
Tracking Performance of Online Stochastic Learners,
SPLetters(27), 2020, pp. 1385-1389.
IEEE DOI 2008
Stochastic processes, Signal processing algorithms, Optimization, Adaptation models, Random variables, Steady-state, non-stationary environment BibRef

Yousef, W.A.[Waleed A.],
Prudence when assuming normality: An advice for machine learning practitioners,
PRL(138), 2020, pp. 44-50.
Elsevier DOI 2010
Bayes classifier, Multinormal distribution, Central limit theorem, Classification, Binormal model BibRef

Aguerri, I.E.[Iñaki Estella], Zaidi, A.[Abdellatif],
Distributed Variational Representation Learning,
PAMI(43), No. 1, January 2021, pp. 120-138.
IEEE DOI 2012
Mutual information, Loss measurement, Complexity theory, Data mining, Approximation algorithms, Data models, information bottleneck BibRef

Guo, Y.[Yuan], Zhang, B.[Bing], Sun, Y., Jiang, K., Wu, K.,
Machine learning based feature selection and knowledge reasoning for CBR system under big data,
PR(112), 2021, pp. 107805.
Elsevier DOI 2102
WRPCSP algorithm, GO algorithm, Case based reasoning, Machine learning BibRef

Terziyan, V.[Vagan], Nikulin, A.[Anton],
Semantics of Voids within Data: Ignorance-Aware Machine Learning,
IJGI(10), No. 4, 2021, pp. xx-yy.
DOI Link 2104
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Luo, Y.[Yan], Wong, Y.K.[Yong-Kang], Kankanhalli, M.S.[Mohan S.], Zhao, Q.[Qi],
Direction Concentration Learning: Enhancing Congruency in Machine Learning,
PAMI(43), No. 6, June 2021, pp. 1928-1946.
IEEE DOI 2106
Task analysis, Visualization, Computational modeling, Training, Convergence, Predictive models, Machine learning, Optimization, congruency BibRef

Luo, Y.[Yan], Wong, Y.K.[Yong-Kang], Kankanhalli, M.S.[Mohan S.], Zhao, Q.[Qi],
Learning to Minimize the Remainder in Supervised Learning,
MultMed(25), 2023, pp. 1738-1748.
IEEE DOI 2306
Training, Task analysis, Standards, Optimization methods, Computational modeling, Stochastic processes, Object detection, supervised learning BibRef

Xie, Y.[Yu], Yu, B.[Bin], Lv, S.Z.[Sheng-Ze], Zhang, C.[Chen], Wang, G.D.[Guo-Dong], Gong, M.G.[Mao-Guo],
A survey on heterogeneous network representation learning,
PR(116), 2021, pp. 107936.
Elsevier DOI 2106
Heterogeneous network, Network representation learning, Machine learning BibRef

Wang, H.[Hui], Zhao, H.B.[Han-Bin], Li, X.[Xi],
Progressive Class-Based Expansion Learning for Image Classification,
SPLetters(28), 2021, pp. 1430-1434.
IEEE DOI 2108
Training, Pipelines, Optimization, Loss measurement, Learning systems, Feature extraction, image classification BibRef

Plotz, T.[Thomas],
Applying Machine Learning for Sensor Data Analysis in Interactive Systems: Common Pitfalls of Pragmatic Use and Ways to Avoid Them,
Surveys(54), No. 6, July 2021, pp. xx-yy.
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Sensor data analysis, machine learning applications BibRef

Nikparvar, B.[Behnam], Thill, J.C.[Jean-Claude],
Machine Learning of Spatial Data,
IJGI(10), No. 9, 2021, pp. xx-yy.
DOI Link 2109
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Liu, Y.Y.[Yuan-Yuan], Shang, F.H.[Fan-Hua], Liu, H.Y.[Hong-Ying], Kong, L.[Lin], Jiao, L.C.[Li-Cheng], Lin, Z.C.[Zhou-Chen],
Accelerated Variance Reduction Stochastic ADMM for Large-Scale Machine Learning,
PAMI(43), No. 12, December 2021, pp. 4242-4255.
IEEE DOI 2112
Convex functions, Large-scale systems, Convergence, Stochastic processes, Optimization, Machine learning, smooth and non-smooth BibRef

Zhu, C.Z.[Cheng-Zhang], Cao, L.B.[Long-Bing], Yin, J.P.[Jian-Ping],
Unsupervised Heterogeneous Coupling Learning for Categorical Representation,
PAMI(44), No. 1, January 2022, pp. 533-549.
IEEE DOI 2112
Couplings, Kernel, Frequency measurement, Complexity theory, Task analysis, Shape, Image color analysis, Coupling learning, unsupervised learning BibRef

Shin, Y.H.[Yu-Hyun], Baek, S.J.[Seung Jun],
Hopfield-type neural ordinary differential equation for robust machine learning,
PRL(152), 2021, pp. 180-187.
Elsevier DOI 2112
Neural ODE, Adversarial defense, Hopfield-type network, Image classification BibRef

Toccaceli, P.[Paolo],
Introduction to conformal predictors,
PR(124), 2022, pp. 108507.
Elsevier DOI 2203
General learning issues. Conformal prediction, Nonparametric methods, Confidence BibRef

Zhang, J.[Jing], Tao, H.[Hong], Luo, T.J.[Ting-Jin], Hou, C.P.[Chen-Ping],
Safe incomplete label distribution learning,
PR(125), 2022, pp. 108518.
Elsevier DOI 2203
Learn importantance of each label to an instance. Label distribution learning, Safeness, Incomplete supervised learning BibRef

Liu, X.J.[Xiao-Jian], Kounadi, O.[Ourania], Zurita-Milla, R.[Raul],
Incorporating Spatial Autocorrelation in Machine Learning Models Using Spatial Lag and Eigenvector Spatial Filtering Features,
IJGI(11), No. 4, 2022, pp. xx-yy.
DOI Link 2205
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Talamantes, A.[Ariana], Chavez, E.[Edgar],
Instance-based learning using the half-space proximal graph,
PRL(156), 2022, pp. 88-95.
Elsevier DOI 2205
Instance based learning, Half space proximal graphs, classififier BibRef

Coskun, K.[Kutalmis], Tümer, B.[Borahan],
An adaptive estimation method with exploration and exploitation modes for non-stationary environments,
PR(129), 2022, pp. 108702.
Elsevier DOI 2206
Stochastic learning, Concept drift, Change detection, Parameter estimation, Dynamic learning rate BibRef

Ahmed, W.[Waqas], Muhammad, K.[Khan], Glass, H.J.[Hylke Jan], Chatterjee, S.[Snehamoy], Khan, A.[Asif], Hussain, A.[Abid],
Novel MLR-RF-Based Geospatial Techniques: A Comparison with OK,
IJGI(11), No. 7, 2022, pp. xx-yy.
DOI Link 2208
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Zhang, X.[Xuan], Jiao, L.[Lei], Granmo, O.C.[Ole-Christoffer], Goodwin, M.[Morten],
On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators,
PAMI(44), No. 10, October 2022, pp. 6345-6359.
IEEE DOI 2209
Machine learning. Convergence, Training, Learning automata, Training data, Task analysis, convergence analysis BibRef

Lin, W.Y.[Wen-Yan], Liu, S.Y.[Si-Ying], Ren, C.H.[Chang-Hao], Cheung, N.M.[Ngai-Man], Li, H.D.[Hong-Dong], Matsushita, Y.[Yasuyuki],
Shell Theory: A Statistical Model of Reality,
PAMI(44), No. 10, October 2022, pp. 6438-6453.
IEEE DOI 2209
Semantics, Mathematical model, Machine learning, Random variables, Manifolds, Machine learning algorithms, Prediction algorithms, generative models BibRef

Xie, J.H.[Jia-Hao], Zhan, X.H.[Xiao-Hang], Liu, Z.W.[Zi-Wei], Ong, Y.S.[Yew-Soon], Loy, C.C.[Chen Change],
Delving into Inter-Image Invariance for Unsupervised Visual Representations,
IJCV(130), No. 12, December 2022, pp. 2994-3013.
Springer DOI 2211
Code, Learning.
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Hsu, H.[Hsiang], Salamatian, S.[Salman], Calmon, F.P.[Flavio P.],
Generalizing Correspondence Analysis for Applications in Machine Learning,
PAMI(44), No. 12, December 2022, pp. 9347-9362.
IEEE DOI 2212
Correlation, Random variables, Data visualization, Principal component analysis, Kernel, Optimization, Hilbert space, multi-modal learning BibRef

Valdes, G.[Gilmer], Friedman, J.H.[Jerome H.], Jiang, F.[Fei], Gennatas, E.D.[Efstathios D.],
Representational Gradient Boosting: Backpropagation in the Space of Functions,
PAMI(44), No. 12, December 2022, pp. 10186-10195.
IEEE DOI 2212
Boosting, Prediction algorithms, Backpropagation, Convolution, Stacking, Predictive models, Machine learning algorithms, neural networks BibRef

Valdes, G.[Gilmer], Interian, Y.[Yannet], Gennatas, E.D.[Efstathios D.], van der Laan, M.[Mark],
The Conditional Super Learner,
PAMI(44), No. 12, December 2022, pp. 10236-10243.
IEEE DOI 2212
Libraries, Mathematical models, Training data, Machine learning algorithms, Stacking, Partitioning algorithms, nonparametric hierarchical models BibRef

Ling, Z.L.[Zhao-Long], Yu, K.[Kui], Zhang, Y.W.[Yi-Wen], Liu, L.[Lin], Li, J.[Jiuyong],
Causal learner: A toolbox for causal structure and Markov blanket learning,
PRL(163), 2022, pp. 92-95.
Elsevier DOI 2212

WWW Link. Causal structure learning, Markov blanket, Bayesian network BibRef

Hao, F.[Fei], Gong, Y.Q.[Yan-Qi], Yu, W.[Wangyang], Loia, V.[Vincenzo],
Knowledge points navigation based on three-way concept lattice for autonomous learning,
PRL(163), 2022, pp. 96-103.
Elsevier DOI 2212
Autonomous learning, Three-way concept lattice, Formal context, Knowledge points navigation BibRef

Lefebvre, T.[Tom],
Information-theoretic policy learning from partial observations with fully informed decision makers,
PRL(164), 2022, pp. 81-88.
Elsevier DOI 2212
Information-theory, Hidden Markov models, Bayesian methods, Imitation learning, Markov decision processes BibRef

Ermolov, A.[Aleksandr], Sangineto, E.[Enver], Sebe, N.[Nicu],
Temporal Alignment for History Representation in Reinforcement Learning,
ICPR22(2172-2178)
IEEE DOI 2212
Representation learning, Visualization, Source coding, Games, Reinforcement learning, Benchmark testing, Streaming media BibRef

Lin, W.Y.[Wen-Yan], Liu, S.Y.[Si-Ying], Dai, B.T.[Bing Tian], Li, H.D.[Hong-Dong],
Distance Based Image Classification: A solution to generative classification's conundrum?,
IJCV(131), No. 1, January 2023, pp. 177-198.
Springer DOI 2301

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Pan, Y.[Yigong], Tang, K.[Ke], Sun, G.Z.[Guang-Zhong],
Theoretical guarantee for crowdsourcing learning with unsure option,
PR(137), 2023, pp. 109316.
Elsevier DOI 2302
Machine learning, Crowdsourcing learning, Labeling BibRef

Zhou, X.C.[Xing-Cai], Chang, L.[Le], Xu, P.F.[Peng-Fei], Lv, S.[Shaogao],
Communication-efficient and Byzantine-robust distributed learning with statistical guarantee,
PR(137), 2023, pp. 109312.
Elsevier DOI 2302
Distributed learning, Byzantine failure, Communication efficiency, Surrogate likelihood, Proximal algorithm BibRef

Lam, F.[Fan], Peng, X.[Xi], Liang, Z.P.[Zhi-Pei],
High-Dimensional MR Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning: Current progress and future directions,
SPMag(40), No. 2, March 2023, pp. 101-115.
IEEE DOI 2303
Systematics, Biological system modeling, Pathological processes, Imaging, Magnetic resonance, Machine learning, Mathematical models, Spectral analysis BibRef

Shi, J.J.[Jin-Jing], Wang, W.X.[Wen-Xuan], Lou, X.P.[Xiao-Ping], Zhang, S.C.[Shi-Chao], Li, X.L.[Xue-Long],
Parameterized Hamiltonian Learning With Quantum Circuit,
PAMI(45), No. 5, May 2023, pp. 6086-6095.
IEEE DOI 2304
Quantum computing, Quantum circuit, Quantum system, Computers, Logic gates, Image segmentation, Quantum state, image segmentation BibRef

Wang, X.K.[Xiao-Kang], Lu, S.[Shan], Zhou, R.[Rui], Wang, H.[Huiwen],
Skeleton estimation of directed acyclic graphs using partial least squares from correlated data,
PR(139), 2023, pp. 109460.
Elsevier DOI 2304
Directed acyclic graph, partial least squares, hierarchical clustering, sparse learning BibRef

Ding, S.F.[Shi-Fei], Du, W.[Wei], Ding, L.[Ling], Guo, L.[Lili], Zhang, J.[Jian], An, B.[Bo],
Multi-agent dueling Q-learning with mean field and value decomposition,
PR(139), 2023, pp. 109436.
Elsevier DOI 2304
Multi-agent, value decomposition, mixed cooperative-competitive task, mean filed BibRef

Kong, H.[He], Wang, L.M.[Li-Min],
Flexible model weighting for one-dependence estimators based on point-wise independence analysis,
PR(139), 2023, pp. 109473.
Elsevier DOI 2304
Point-wise independence analysis, Independence assumption, Point-wise log likelihood, Weighted one-dependence estimators BibRef

Zhang, H.W.[Hong-Wei], Ye, X.Y.[Xiao-Yu], Hu, Q.[Qi],
Spatiotemporal Learning via Mixture Importance Gaussian Filtering With Sparse Regularization,
SPLetters(30), 2023, pp. 279-283.
IEEE DOI 2304
Extraterrestrial measurements, Spatiotemporal phenomena, Estimation, Noise measurement, Uncertainty, constraint uncertainty BibRef

Jegorova, M.[Marija], Kaul, C.[Chaitanya], Mayor, C.[Charlie], O'Neil, A.Q.[Alison Q.], Weir, A.[Alexander], Murray-Smith, R.[Roderick], Tsaftaris, S.A.[Sotirios A.],
Survey: Leakage and Privacy at Inference Time,
PAMI(45), No. 7, July 2023, pp. 9090-9108.
IEEE DOI 2306
Data models, Data privacy, Task analysis, Training, Computational modeling, Training data, Glass box, Data leakage, adversarial defences BibRef

Fu, C.[Cui], Zhou, S.S.[Shui-Sheng], Chen, Y.[Yuxue], Chen, L.[Li], Han, B.[Banghe],
A risk-averse learning machine via variance-dependent penalization,
PRL(171), 2023, pp. 116-123.
Elsevier DOI 2306
Hoeffding's inequality, Empirical Bernstein bound, Sample variance penalization, Standard deviation, Newton algorithms BibRef

Freitas, S.[Sergio], Laber, E.[Eduardo], Lazera, P.[Pedro], Molinaro, M.[Marco],
Time-constrained learning,
PR(142), 2023, pp. 109672.
Elsevier DOI 2307
Machine teaching, Time-constrained learning, Classification methods BibRef

Mohr, F.[Felix], van Rijn, J.N.[Jan N.],
Fast and Informative Model Selection Using Learning Curve Cross-Validation,
PAMI(45), No. 8, August 2023, pp. 9669-9680.
IEEE DOI 2307
Validation of the learned results, more efficient. Data models, Training, Predictive models, Computational modeling, Machine learning, Runtime, Prediction algorithms, Decision making, supervised machine learning BibRef

Cao, X.F.[Xiao-Feng], Liu, W.Y.[Wei-Yang], Tsang, I.W.[Ivor W.],
Data-Efficient Learning via Minimizing Hyperspherical Energy,
PAMI(45), No. 11, November 2023, pp. 13422-13437.
IEEE DOI 2310
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Li, Z.B.[Zhi-Bin], Koniusz, P.[Piotr], Zhang, L.[Lu], Pagendam, D.E.[Daniel Edward], Moghadam, P.[Peyman],
Exploiting Field Dependencies for Learning on Categorical Data,
PAMI(45), No. 11, November 2023, pp. 13509-13522.
IEEE DOI 2310
dependencise between fields, not just embedding of data points. BibRef

Achituve, I.[Idan], Wang, W.B.[Wen-Bo], Fetaya, E.[Ethan], Leshem, A.[Amir],
Communication Efficient Distributed Learning Over Wireless Channels,
SPLetters(30), 2023, pp. 1402-1406.
IEEE DOI 2310
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Fu, S.[Saiji], Dong, T.Y.[Tian-Yi], Wang, Z.X.[Zhao-Xin], Tian, Y.J.[Ying-Jie],
Weakly privileged learning with knowledge extraction,
PR(153), 2024, pp. 110517.
Elsevier DOI 2405
Learning using privileged information (LUPI), Weakly privileged learning, Knowledge extraction, Classification BibRef

Li, T.[Tao], Meng, C.[Cheng], Xu, H.T.[Hong-Teng], Yu, J.[Jun],
Hilbert Curve Projection Distance for Distribution Comparison,
PAMI(46), No. 7, July 2024, pp. 4993-5007.
IEEE DOI 2406
Task analysis, Data models, Probability distribution, Couplings, Costs, Computational modeling, Big Data, Distribution comparison, projection robust Wasserstein distance BibRef

Gao, Y.[Yi], Xu, M.[Miao], Zhang, M.L.[Min-Ling],
Complementary to Multiple Labels: A Correlation-Aware Correction Approach,
PAMI(46), No. 12, December 2024, pp. 9179-9191.
IEEE DOI 2411
Correlation, Matrix decomposition, Training, Vectors, Noise measurement, Semantics, Reviews, transition matrix BibRef

Wang, X.[Xin], Chen, H.[Hong], Tang, S.[Si'ao], Wu, Z.[Zihao], Zhu, W.W.[Wen-Wu],
Disentangled Representation Learning,
PAMI(46), No. 12, December 2024, pp. 9677-9696.
IEEE DOI 2411
Image color analysis, Task analysis, Semantics, Shape, Data models, Computational modeling, disentangled representation learning, representation learning BibRef

Qu, B.[Bohao], Cao, X.F.[Xiao-Feng], Chang, Y.[Yi], Tsang, I.W.[Ivor W.], Ong, Y.S.[Yew-Soon],
Diversifying Policies With Non-Markov Dispersion to Expand the Solution Space,
PAMI(46), No. 12, December 2024, pp. 11392-11408.
IEEE DOI 2411
Dispersion, Trajectory, Transformers, Reinforcement learning, Logic, Standards, Markov decision processes, solution space BibRef

Miani, M.[Marco], Parton, M.[Maurizio], Romito, M.[Marco],
Curious Explorer: A Provable Exploration Strategy in Policy Learning,
PAMI(46), No. 12, December 2024, pp. 11422-11431.
IEEE DOI 2411
Picture archiving and communication systems, Optimization, Mathematical models, Iterative methods, Bayes methods, reinforcement learning BibRef

Zahrae, F.F.[Fatouchi Fatima], Es-Sâadia, A.[Aoula], Mohamed, Y.[Youssfi],
Artificial Intelligence Implementation in Virtual Learning Environment: An Overview,
ISCV24(1-6)
IEEE DOI 2408
Deep learning, Electronic learning, Pandemics, Reviews, Globalization, Virtual environments, Artificial intelligent, Adaptative learning BibRef

Lu, Y.Z.[Yu-Zhe], Liu, X.R.[Xin-Ran], Soltoggio, A.[Andrea], Kolouri, S.[Soheil],
SLoSH: Set Locality Sensitive Hashing via Sliced-Wasserstein Embeddings,
WACV24(2554-2564)
IEEE DOI 2404
Point cloud compression, Codes, Sensitivity analysis, Parallel processing, Nearest neighbor methods, Vectors, Algorithms BibRef

Zhang, J.H.[Jia-Hao], Wang, B.[Bowen], Li, L.Z.[Liang-Zhi], Nakashima, Y.[Yuta], Nagahara, H.[Hajime],
Instruct Me More! Random Prompting for Visual In-Context Learning,
WACV24(2585-2594)
IEEE DOI Code:
WWW Link. 2404
Technique from natural language processing. Training, Visualization, Computational modeling, Perturbation methods, Object detection, Interference, Algorithms, Image recognition and understanding BibRef

Wen, C.S.[Chang-Song], Zhang, X.[Xin], Yao, X.X.[Xing-Xu], Yang, J.F.[Ju-Feng],
Ordinal Label Distribution Learning,
ICCV23(23424-23434)
IEEE DOI Code:
WWW Link. 2401
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Cui, J.L.[Jia-Li], Wu, Y.N.[Ying Nian], Han, T.[Tian],
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator,
CVPR23(3603-3612)
IEEE DOI 2309
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Ai, H.[Hao], Liao, Q.M.[Qing-Min], Chen, Y.Y.[Yi-Yun], Qian, J.[Jiang],
Gaussian Mixture Distribution Makes Data Uncertainty Learning Better,
FG21(01-08)
IEEE DOI 2303
Learning systems, Interference suppression, Uncertainty, Face recognition, Estimation, Benchmark testing BibRef

Sahu, P.[Pritish], Basioti, K.[Kalliopi], Pavlovic, V.[Vladimir],
DAReN: A Collaborative Approach Towards Visual Reasoning And Disentangling,
ICPR22(4448-4455)
IEEE DOI 2212
Measurement, Visualization, Correlation, Graphical models, Collaboration, Benchmark testing, Cognition BibRef

Roychowdhury, S., Sontakke, S.A., Itti, L., Sarkar, M., Aggarwal, M., Badjatiya, P., Puri, N., Krishnamurthy, B.,
SHERLock: Self-Supervised Hierarchical Event Representation Learning,
ICPR22(2672-2678)
IEEE DOI 2212
Training, Representation learning, Visualization, Grounding, Video sequences, Tutorials, Reinforcement learning BibRef

Ramasinghe, S.[Sameera], Lucey, S.[Simon],
Beyond Periodicity: Towards a Unifying Framework for Activations in Coordinate-MLPs,
ECCV22(XXXIII:142-158).
Springer DOI 2211
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He, Y.N.[Yi-Nan], Huang, G.S.[Geng-Shi], Chen, S.[Siyu], Teng, J.N.[Jia-Ning], Wang, K.[Kun], Yin, Z.F.[Zhen-Fei], Sheng, L.[Lu], Liu, Z.W.[Zi-Wei], Qiao, Y.[Yu], Shao, J.[Jing],
X-Learner: Learning Cross Sources and Tasks for Universal Visual Representation,
ECCV22(XXVI:509-528).
Springer DOI 2211
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Kondapaneni, N.[Neehar], Perona, P.[Pietro], Aodha, O.M.[Oisin Mac],
Visual Knowledge Tracing,
ECCV22(XXV:415-431).
Springer DOI 2211
Evaluation and datasets for how people learn new categorization tasks. BibRef

Zhang, D.J.H.[David Jun-Hao], Li, K.[Kunchang], Wang, Y.[Yali], Chen, Y.P.[Yun-Peng], Chandra, S.[Shashwat], Qiao, Y.[Yu], Liu, L.Q.[Luo-Qi], Shou, M.Z.[Mike Zheng],
MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning,
ECCV22(XXXV:230-248).
Springer DOI 2211
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Xiong, Y.H.[Yuan-Hao], Hsieh, C.J.[Cho-Jui],
Learning to Learn with Smooth Regularization,
ECCV22(XXIII:550-565).
Springer DOI 2211
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Kim, K.I.[Kwang In],
Robust Combination of Distributed Gradients Under Adversarial Perturbations,
CVPR22(254-263)
IEEE DOI 2210
Manifolds, Computer aided instruction, Privacy, Machine learning algorithms, Distance learning, Privacy and federated learning BibRef

Chen, X.L.[Xin-Lei], He, K.M.[Kai-Ming],
Exploring Simple Siamese Representation Learning,
CVPR21(15745-15753)
IEEE DOI 2111
Maximize the similarity between two augmentations of one image. Visualization, Codes, Shape, Computational modeling, Tools BibRef

Yan, S.[Sijie], Xiong, Y.J.[Yuan-Jun], Kundu, K.[Kaustav], Yang, S.[Shuo], Deng, S.Q.[Si-Qi], Wang, M.[Meng], Xia, W.[Wei], Soatto, S.[Stefano],
Positive-Congruent Training: Towards Regression-Free Model Updates,
CVPR21(14294-14303)
IEEE DOI 2111
Training, Deep learning, Error analysis, Computational modeling, Predictive models BibRef

Epstein, D.[Dave], Vondrick, C.[Carl],
Learning Goals from Failure,
CVPR21(11189-11199)
IEEE DOI 2111
Visualization, Psychology, Predictive models, Encoding, Trajectory BibRef

Sarfraz, F.[Fahad], Arani, E.[Elahe], Zonooz, B.[Bahram],
Noisy Concurrent Training for Efficient Learning under Label Noise,
WACV21(3158-3167)
IEEE DOI 2106
Training, Filtering, Buildings, Supervised learning, Fitting, Brain modeling BibRef

Pranavan, T.[Theivendiram], Sim, T.[Terence],
Learning with Delayed Feedback,
ICPR21(4895-4902)
IEEE DOI 2105
Image segmentation, Impedance matching, Semantics, Machine learning, Task analysis BibRef

Guo, X.F.[Xi-Feng], Liu, J.Y.[Ji-Yuan], Zhou, S.H.[Si-Hang], Zhu, E.[En], Dong, S.H.[Shi-Hao],
Image Representation Learning by Transformation Regression,
ICPR21(526-533)
IEEE DOI 2105
Training, Image edge detection, Machine learning, Image representation, Predictive models, Jitter BibRef

Eldeeb, H.[Hassan], Amashukeli, S.[Shota], El Shawi, R.[Radwa],
An Empirical Analysis of Integrating Feature Extraction to Automated Machine Learning Pipeline,
HCAU20(336-344).
Springer DOI 2103
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Yang, Y., Zhu, D., Ren, F., Cheng, C.,
A Novel Self-taught Learning Framework Using Spatial Pyramid Matching For Scene Classification,
ISPRS20(B2:725-729).
DOI Link 2012
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Henke, K., Kenyon, G.T., Migliori, B.,
Machine Learning in a Post Moore's Law World: Quantum vs. Neuromorphic Substrates,
SSIAI20(74-77)
IEEE DOI 2009
competitive algorithms, graph theory, learning (artificial intelligence), neuromorphic engineering, sparse coding BibRef

Liu, J.X.[Jin-Xian], Ni, B.B.[Bing-Bing], Li, C.Y.[Cai-Yuan], Yang, J.C.[Jian-Cheng], Tian, Q.[Qi],
Dynamic Points Agglomeration for Hierarchical Point Sets Learning,
ICCV19(7545-7554)
IEEE DOI 2004
convolutional neural nets, learning (artificial intelligence), mathematics computing, pattern classification, Machine learning BibRef

Zhou, L.J.[Lin-Jun], Cui, P.[Peng], Yang, S.Q.[Shi-Qiang], Zhu, W.W.[Wen-Wu], Tian, Q.[Qi],
Learning to Learn Image Classifiers With Visual Analogy,
CVPR19(11489-11498).
IEEE DOI 2002
Especially by analogy. BibRef

Tian, K.[Kai], Xu, Y.[Yi], Zhou, S.G.[Shui-Geng], Guan, J.H.[Ji-Hong],
Versatile Multiple Choice Learning and Its Application to Vision Computing,
CVPR19(6342-6350).
IEEE DOI 2002
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Wu, Y.[Yue], Chen, Y.P.[Yin-Peng], Wang, L.J.[Li-Juan], Ye, Y.C.[Yuan-Cheng], Liu, Z.C.[Zi-Cheng], Guo, Y.D.[Yan-Dong], Fu, Y.[Yun],
Large Scale Incremental Learning,
CVPR19(374-382).
IEEE DOI 2002
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Elhoseiny, M.[Mohamed], Babiloni, F.[Francesca], Aljundi, R.[Rahaf], Rohrbach, M.[Marcus], Paluri, M.[Manohar], Tuytelaars, T.[Tinne],
Exploring the Challenges Towards Lifelong Fact Learning,
ACCV18(VI:66-84).
Springer DOI 1906
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Faria, F.A.[Fabio Augusto], Sarkar, S.[Sudeep],
A Graph-based Approach for Static Ensemble Selection in Remote Sensing Image Analysis,
ICPR18(344-349)
IEEE DOI 1812
Support vector machines, Remote sensing, Gases, Task analysis, Agriculture, Bagging, Computer architecture BibRef

Guo, M.[Michelle], Haque, A.[Albert], Huang, D.A.[De-An], Yeung, S.[Serena], Fei-Fei, L.[Li],
Dynamic Task Prioritization for Multitask Learning,
ECCV18(XVI: 282-299).
Springer DOI 1810
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Rupprecht, C., Laina, I., di Pietro, R., Baust, M.,
Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses,
ICCV17(3611-3620)
IEEE DOI 1802
image classification, learning (artificial intelligence), object detection, pose estimation, regression analysis, MHP models, Uncertainty BibRef

Doersch, C.[Carl], Zisserman, A.,
Multi-task Self-Supervised Visual Learning,
ICCV17(2070-2079)
IEEE DOI 1802
image classification, image representation, learning (artificial intelligence), object detection, Training BibRef

Goyal, P.[Priya], Mahajan, D.[Dhruv], Gupta, A.[Abhinav], Misra, I.[Ishan],
Scaling and Benchmarking Self-Supervised Visual Representation Learning,
ICCV19(6390-6399)
IEEE DOI 2004
Code, Learning.
WWW Link. image representation, object detection, supervised learning, self-supervised learning, Navigation BibRef

Wang, X.L.[Xiao-Long], He, K.M.[Kai-Ming], Gupta, A.[Abhinav],
Transitive Invariance for Self-Supervised Visual Representation Learning,
ICCV17(1338-1347)
IEEE DOI 1802
image classification, image representation, learning (artificial intelligence), object detection, Visualization BibRef

Adam, N.L.[Noor Latiffah], Alzahri, F.B.[Fatin Balkis], Soh, S.C.[Shaharuddin Cik], Bakar, N.A.[Nordin Abu], Kamal, N.A.M.[Nor Ashikin Mohamad],
Self-Regulated Learning and Online Learning: A Systematic Review,
IVIC17(143-154).
Springer DOI 1711
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Ramicic, M.[Mirza], Bonarini, A.[Andrea],
Entropy-based prioritized sampling in Deep Q-learning,
ICIVC17(1068-1072)
IEEE DOI 1708
Entropy, Markov decision processes, entropy, neural networks, reinforcement learning BibRef

Cuevas, J.S.[Jonathan Serrano], Manzanares, E.M.[Eduardo Morales],
An Exploration Strategy for RL with Considerations of Budget and Risk,
MCPR17(105-116).
Springer DOI 1706
Reinforcement Learning BibRef

Zhang, C.[Cheng], Kjellström, H.[Hedvig], Ek, C.H.[Carl Henrik],
Inter-battery Topic Representation Learning,
ECCV16(VIII: 210-226).
Springer DOI 1611
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Earlier: A1, A2, Only:
How to Supervise Topic Models,
GMCV14(500-515).
Springer DOI 1504
supervised topic models using Supervised Latent Dirichlet Allocation BibRef

Li, C.S.[Chang-Sheng], Yang, L.[Lin], Liu, Q.S.[Qing-Shan], Meng, F.J.[Fan-Jing], Dong, W.S.[Wei-Shan], Wang, Y.[Yu], Xu, J.M.[Jing-Min],
Multiple-Output Regression with High-Order Structure Information,
ICPR14(3868-3873)
IEEE DOI 1412
Correlation BibRef

Wang, Z.H.[Zi-Heng], Wang, X.Y.[Xiao-Yang], Ji, Q.A.[Qi-Ang],
Learning with Hidden Information,
ICPR14(238-243)
IEEE DOI 1412
Equations BibRef

Aodha, O.M.[Oisin Mac], Stathopoulos, V.[Vassilios], Brostow, G.J.[Gabriel J.], Terry, M.[Michael], Girolami, M.[Mark], Jones, K.E.[Kate E.],
Putting the Scientist in the Loop: Accelerating Scientific Progress with Interactive Machine Learning,
ICPR14(9-17)
IEEE DOI 1412
Biological system modeling BibRef

Grenier, P.A.[Pierre-Anthony], Brun, L.[Luc], Villemin, D.[Didier],
A Graph Kernel Incorporating Molecule's Stereisomerism Information,
ICPR14(631-636)
IEEE DOI 1412
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Incorporating Molecule's Stereisomerism within the Machine Learning Framework,
SSSPR14(12-21).
Springer DOI 1408
Adaptive optics BibRef

Pahikkala, T.[Tapio],
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SSSPR16(74-83).
Springer DOI 1611
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CIARP13(I:294-301).
Springer DOI 1311
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Earlier: A1, A3, A4, A2:
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CIARP12(503-510).
Springer DOI 1209
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ICPR12(1715-1718).
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Mittal, A.[Arpit], Blaschko, M.B.[Matthew B.], Zisserman, A.[Andrew], Torr, P.H.S.[Philip H. S.],
Taxonomic Multi-class Prediction and Person Layout Using Efficient Structured Ranking,
ECCV12(II: 245-258).
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ECCV12(II: 202-215).
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ACCV12(I:274-288).
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IEEE DOI 1008
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Rottmann, A.[Axel], Burgard, W.[Wolfram],
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ICPR06(IV: 639-642).
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IEEE DOI 0609
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Laven, K., Leishman, S., Roweis, S.,
A statistical learning approach to document image analysis,
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Mulligan, J., Grudic, G.,
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IEEE DOI 0507
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Xiong, H.L.[Hui-Lin], Swamy, M.N.S., Ahmad, M.O.,
Learning with the Optimized Data-Dependent Kernel,
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Qin, A.K., Suganthan, P.N.,
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Ko, J.[Jaepil], Kim, E.[Eunju], Byun, H.R.[Hye-Ran],
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Leang, P., Bhanu, B.,
Learning integrated perception-based speed control,
ICPR04(I: 813-816).
IEEE DOI 0409
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Taylor, G.W.,
A reinforcement learning framework for parameter control in computer vision applications,
CRV04(496-503).
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Miasnikov, A.D., Rome, J.E., Haralick, R.M.,
A hierarchical projection pursuit clustering algorithm,
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Shi, D., Ng, G.S., Gao, J., Yeung, D.S.,
Critical vector learning to construct RBF classifiers,
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IEEE DOI 0409
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Eriksson, M.[Martin], Carlsson, S.[Stefan],
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SCIA03(792-799).
Springer DOI 0310
Model the set of possible events by algebraic inequalities. Learning proceeds by selecting those inequalities that show a consistent sign when applied to the training data set. The manifold of possible events estimated in this way will in general represent the qualitative properties of the events. BibRef

Takamatsu, J.[Jun],
Improving State Based Analysis for Learning from Observation,
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Kubota, S., Mizutani, H., Kurosawa, Y.,
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Mizutani, H.[Hiroyuki],
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Naphade, M.R., Frey, B.J., Chen, L., Huang, T.S.,
Learning Sparse Multiple Cause Models,
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IEEE DOI 0009
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Hong, P., Huang, T.S.,
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Sato, A.[Atsushi],
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Sato, A.,
A Learning Method for Definite Canonicalization Based on Minimum Classification Error,
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IEEE DOI 0009
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General Bias/Variance Decomposition with Target Independent Variance of Error Functions Derived from the Exponential Family of Distributions,
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Brooks, R.A., Grimson, W.E.L., Poggio, T., Koch, C., Sodini, C., Stein, L., Yang, W.,
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Teller, A.[Astro], Veloso, M.[Manuela],
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IEEE DOI 9608
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Rong, S., Bhanu, B.,
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IEEE DOI 9208
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Soloway, E.M., Riseman, E.M.,
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IJCAI77(801-811).Cp

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Learning, General Non-Vision Learning Issues .