14.5 Learning in Computer Vision

Chapter Contents (Back)
Learning. See also Imbalanced Sample Sizes, Imbalanced Data.

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.
WWW Link. 0309
BibRef

Shimura, M.[Masamichi],
Recognizing machines with parametric and nonparametric learning methods using contextual information,
PR(5), No. 2, June 1973, pp. 149-168.
WWW Link. 0309
BibRef

Imai, T.[Toshio], Shimura, M.[Masamichi],
Learning with probabilistic labeling,
PR(8), No. 1, January 1976, pp. 5-10.
WWW Link. 0309
BibRef

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.
WWW Link. 0309
BibRef

Williams, H.[Harold],
A net-structure learning system for pattern description,
PR(8), No. 4, October 1976, pp. 261-271.
WWW Link. 0309
BibRef

Coulon, D.[Daniel], Kayser, D.[Daniel],
Learning criterion and inductive behaviour,
PR(10), No. 1, 1978, pp. 19-25.
WWW Link. 0309
BibRef

Gowda, K.C.[K. Chidananda], Krishna, G.,
Learning with a mutualistic teacher,
PR(11), No. 5-6, 1979, pp. 383-390.
WWW Link. 0309
BibRef

Takiyama, R.[Ryuzo],
A learning procedure for multisurface method of pattern separation,
PR(12), No. 2, 1980, pp. 75-82.
WWW Link. 0309
BibRef

Greblicki, W.[Wlodzimierz],
Learning to recognize patterns with a probabilistic teacher,
PR(12), No. 3, 1980, pp. 159-164.
WWW Link. 0309
BibRef

Chittineni, C.B.,
Learning with imperfectly labeled patterns,
PR(12), No. 5, 1980, pp. 281-291.
WWW Link. 0309
BibRef

Krishnan, T.,
Efficiency of learning with imperfect supervision,
PR(21), No. 2, 1988, pp. 183-188.
WWW Link. 0309
BibRef

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
BibRef

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.
WWW Link. 0401
BibRef

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.
WWW Link. 0401
BibRef

Lugosi, G.[Gábor],
Learning with an unreliable teacher,
PR(25), No. 1, January 1992, pp. 79-87.
WWW Link. 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.
WWW Link. 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
BibRef

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
BibRef

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
BibRef

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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.,
Sign-based learning schemes for pattern classification,
PRL(26), No. 12, September 2005, pp. 1926-1936.
WWW Link. 0508
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.
WWW Link. 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
Electronic mail BibRef

Lopes, N.[Noel], Ribeiro, B.[Bernardete],
Towards adaptive learning with improved convergence of deep belief networks on graphics processing units,
PR(47), No. 1, 2014, pp. 114-127.
Elsevier DOI 1310
BibRef
And: A2, A2:
Extreme Learning Classifier with Deep Concepts,
CIARP13(I:182-189).
Springer DOI 1311
Deep learning 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.[Yujin], Wang, Z.[Zhe], Gao, D.[Daqi],
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

Lv, F.M.[Feng-Mao], Yang, G.[Guowu], 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


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

Guo, L., Liew, A.W.C.[Alan Wee-Chung],
An Improved Extreme Learning Machine with Parallelized Feature Mapping Structures,
DICTA16(1-5)
IEEE DOI 1701
Diabetes BibRef

Zhang, C.[Cheng], Kjellström, H.[Hedvig], Ek, C.H.[Carl Henrik],
Inter-battery Topic Representation Learning,
ECCV16(VIII: 210-226).
Springer DOI 1611
BibRef
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

Cao, L.L.[Le-Le], Huang, W.B.[Wen-Bing], Sun, F.C.[Fu-Chun],
Optimization-Based Extreme Learning Machine with Multi-kernel Learning Approach for Classification,
ICPR14(3564-3569)
IEEE DOI 1412
Kernel 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
BibRef
Earlier:
Incorporating Molecule's Stereisomerism within the Machine Learning Framework,
SSSPR14(12-21).
Springer DOI 1408
Adaptive optics BibRef

Pahikkala, T.[Tapio],
Fast Gradient Computation for Learning with Tensor Product Kernels and Sparse Training Labels,
SSSPR14(123-132).
Springer DOI 1408
BibRef

Orozco-Alzate, M.[Mauricio], Duin, R.P.W.[Robert P. W.], Bicego, M.[Manuele],
Unsupervised Parameter Estimation of Non Linear Scaling for Improved Classification in the Dissimilarity Space,
SSSPR16(74-83).
Springer DOI 1611
BibRef

Plasencia-Calaña, Y.[Yenisel], Orozco-Alzate, M.[Mauricio], García-Reyes, E.B.[Edel B.], Duin, R.P.W.[Robert P.W.],
Towards Cluster-Based Prototype Sets for Classification in the Dissimilarity Space,
CIARP13(I:294-301).
Springer DOI 1311
BibRef
Earlier: A1, A3, A4, A2:
On Using Asymmetry Information for Classification in Extended Dissimilarity Spaces,
CIARP12(503-510).
Springer DOI 1209
BibRef

Duin, R.P.W.[Robert P. W.], Bicego, M.[Manuele], Orozco-Alzate, M.[Mauricio], Kim, S.W.[Sang-Woon], Loog, M.[Marco],
Metric Learning in Dissimilarity Space for Improved Nearest Neighbor Performance,
SSSPR14(183-192).
Springer DOI 1408
BibRef

Shao, Z.F.[Zhi-Fei], Er, M.J.[Meng Joo], Huang, G.B.[Guang-Bin],
Receding Horizon Cache and Extreme Learning Machine based Reinforcement Learning,
ICARCV12(1591-1596).
IEEE DOI 1304
BibRef

Zhao, Y.[Yue], Yang, G.S.[Guo-Sheng], Xu, X.N.[Xiao-Na], Ji, Q.A.[Qi-Ang],
A near-optimal non-myopic active learning method,
ICPR12(1715-1718).
WWW Link. 1302
BibRef

Blaschko, M.B.[Matthew B.], Mittal, A.[Arpit], Rahtu, E.[Esa],
An O(nlogn) Cutting Plane Algorithm for Structured Output Ranking,
GCPR14(132-143).
Springer DOI 1411
BibRef

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).
Springer DOI 1210
BibRef

Park, K.[Kyoungup], Gould, S.[Stephen],
On Learning Higher-Order Consistency Potentials for Multi-class Pixel Labeling,
ECCV12(II: 202-215).
Springer DOI 1210
BibRef

Armando, C.S.E.[Catalán-Salgado Edgar], Cornelio, Y.M.[Yáñez-Márquez], Jesus, F.N.[Figueroa-Nazuno],
Significative Learning Using Alpha-beta Associative Memories,
CIARP12(535-542).
Springer DOI 1209
BibRef

Chen, L.[Lin], Duan, L.X.[Li-Xin], Tsang, I.W.H.[Ivor W.H.], Xu, D.[Dong],
Efficient Discriminative Learning of Class Hierarchy for Many Class Prediction,
ACCV12(I:274-288).
Springer DOI 1304
BibRef

Bonneau, R.J.[Robert J.], Bonneau, S.G.[Sonya G.],
Sparse linearized iterative coherence estimation (SLICE) and risk assessment in image analysis,
AIPR11(1-7).
IEEE DOI 1204
Learning. BibRef

Pankov, S.[Sergey],
Learning Image Transformations without Training Examples,
ISVC11(II: 168-179).
Springer DOI 1109
Learning affine and elastic transformations when no examples are given. BibRef

Millán-Giraldo, M.[Mónica], Traver, V.J.[Vicente Javier], Sánchez, J.S.[J. Salvador],
On-Line Classification of Data Streams with Missing Values Based on Reinforcement Learning,
IbPRIA11(355-362).
Springer DOI 1106
BibRef

Du, R.[Ruo], Wu, Q.A.[Qi-Ang], He, X.J.[Xiang-Jian], Yang, J.[Jie],
Object Categorization Based on a Supervised Mean Shift Algorithm,
ECCVDemos12(III: 611-614).
Springer DOI 1210
BibRef

Engel, P.M.[Paulo Martins], Heinen, M.R.[Milton Roberto],
Concept Formation Using Incremental Gaussian Mixture Models,
CIARP10(128-135).
Springer DOI 1011
Apply to sonar data streams to learn concepts such as wall, and curve. BibRef

Amores, J.[Jaume],
Vocabulary-Based Approaches for Multiple-Instance Data: A Comparative Study,
ICPR10(4246-4250).
IEEE DOI 1008
BibRef

Jia, K.[Ke], Cheng, L.[Li], Liu, N.J.[Nian-Jun], Wang, L.[Lei],
Efficient Learning to Label Images,
ICPR10(942-945).
IEEE DOI 1008
BibRef

Li, C.G.[Chun-Guang], Guo, J.[Jun], Zhang, H.G.[Hong-Gang],
Local Sparse Representation Based Classification,
ICPR10(649-652).
IEEE DOI 1008
Sparse decomposition in local areas. BibRef

Wójcik, K.[Krzysztof],
Inductive Learning Methods in the Simple Image Understanding System,
ICCVG10(I: 97-104).
Springer DOI 1009
BibRef

Rottmann, A.[Axel], Burgard, W.[Wolfram],
Learning Non-stationary System Dynamics Online Using Gaussian Processes,
DAGM10(192-201).
Springer DOI 1009
BibRef

Tang, K.D.[Kevin D.], Tappen, M.F.[Marshall F.], Sukthankar, R.[Rahul], Lampert, C.H.[Christoph H.],
Optimizing one-shot recognition with micro-set learning,
CVPR10(3027-3034).
IEEE DOI 1006
Learn from single example. BibRef

Wolf, L.B.[Lior B.], Manor, N.[Nathan],
Visual recognition using mappings that replicate margins,
CVPR10(810-816).
IEEE DOI 1006
Learning the map between vector spaces given a pair of matches. Asymmetric case, one more informative than the other. BibRef

Siddiquie, B.[Behjat], Gupta, A.[Abhinav],
Beyond active noun tagging: Modeling contextual interactions for multi-class active learning,
CVPR10(2979-2986).
IEEE DOI Video of talk:
WWW Link. 1006
BibRef

Lin, Y.Y.[Yen-Yu], Tsai, J.F.[Jyun-Fan], Liu, T.L.[Tyng-Luh],
Efficient discriminative local learning for object recognition,
ICCV09(598-605).
IEEE DOI 0909
BibRef

Gupta, A.[Abhinav], Shi, J.B., Davis, L.S.[Larry S.],
A 'shape aware' model for semi-supervised learning of objects and its context,
NIPS08(xx-yy). BibRef 0800

Armstrong, A., Bock, P.,
Using Tactic-Based Learning (formerly Mentoring) to Accelerate Recovery of an Adaptive Learning System in a Changing Environment,
AIPR07(31-36).
IEEE DOI 0710
BibRef

Gabrys, B.[Bogdan],
Learning with Missing or Incomplete Data,
CIAP09(1-4).
Springer DOI 0909
BibRef

Matwin, S.[Stan],
Image Analysis and Machine Learning: How to Foster a Stronger Connection?,
CIAP09(5).
Springer DOI 0909
BibRef

Ni, K.S.[Karl S.], Nguyen, T.Q.[Truong Q.],
A model for image patch-based algorithms,
ICIP08(2588-2591).
IEEE DOI 0810
learning features based on fixed size image patches. BibRef

Leopold, T., Kern-Isberner, G., Peters, G.[Georg],
Combining Reinforcement Learning and Belief Revision: A Learning System for Active Vision,
BMVC08(xx-yy).
PDF File. 0809
BibRef

Domke, J.[Justin], Karapurkar, A.[Alap], Aloimonos, Y.[Yiannis],
Who killed the directed model?,
CVPR08(1-8).
IEEE DOI 0806
MRF (an undirected model) has become popular. Learning can be expensive. Directed models require ordering variables. Learning is relatively simple. Applied to inpainting. BibRef

Nomiya, H.[Hiroki], Uehara, K.[Kuniaki],
Multistrategical Approach in Visual Learning,
ACCV07(I: 502-511).
Springer DOI 0711
BibRef

Zheng, Y.[Yu], Luo, S.W.[Si-Wei], Lv, Z.[Ziang],
Control Double Inverted Pendulum by Reinforcement Learning with Double CMAC Network,
ICPR06(IV: 639-642).
IEEE DOI 0609
BibRef

Autio, I.[Ilkka], Lindgren, J.T.,
Online Learning of Discriminative Patterns from Unlimited Sequences of Candidates,
ICPR06(II: 437-440).
IEEE DOI 0609
BibRef

Maree, R.[Raphael], Stevens, B.[Benjamin], Geurts, P.[Pierre], Guern, Y.[Yves], Mack, P.[Philippe],
A machine learning approach for material detection in hyperspectral images,
OTCBVS09(106-111).
IEEE DOI 0906
BibRef

Ernst, D.[Damien], Marée, R.[Raphaël], Wehenkel, L.[Louis],
Reinforcement Learning with Raw Image Pixels as Input State,
IWICPAS06(446-454).
Springer DOI 0608
BibRef

Laven, K., Leishman, S., Roweis, S.,
A statistical learning approach to document image analysis,
ICDAR05(I: 357-361).
IEEE DOI 0508
BibRef

Mulligan, J., Grudic, G.,
Topological Mapping from Image Sequences,
LCV05(III: 43-43).
IEEE DOI 0507
BibRef

Xiong, H.L.[Hui-Lin], Swamy, M.N.S., Ahmad, M.O.,
Learning with the Optimized Data-Dependent Kernel,
LCV04(95).
IEEE DOI 0406
BibRef

Li, Y.[Yi], Shapiro, L.G.[Linda G.], Bilmes, J.A.[Jeff A.],
A Generative/Discriminative Learning Algorithm for Image Classification,
ICCV05(II: 1605-1612).
IEEE DOI 0510
BibRef

Leitner, R.[Raimund],
Learning 3D Object Recognition from an Unlabelled and Unordered Training Set,
ISVC07(I: 644-651).
Springer DOI 0711
BibRef

Leitner, R.[Raimund], Bischof, H.[Horst],
Recognition of 3D Objects by Learning from Correspondences in a Sequence of Unlabeled Training Images,
DAGM05(369).
Springer DOI 0509
BibRef

Muñoz, X.[Xavier], Bosch, A.[Anna], Martí, J.[Joan], Espunya, J.[Joan],
A Learning Framework for Object Recognition on Image Understanding,
IbPRIA05(II:311).
Springer DOI 0509
BibRef

Bekel, H.[Holger], Bax, I.[Ingo], Heidemann, G.[Gunther], Ritter, H.[Helge],
Adaptive Computer Vision: Online Learning for Object Recognition,
DAGM04(447-454).
Springer DOI 0505
BibRef

Qin, A.K., Suganthan, P.N.,
A novel kernel prototype-based learning algorithm,
ICPR04(IV: 621-624).
IEEE DOI 0409
BibRef

Ko, J.[Jaepil], Kim, E.[Eunju], Byun, H.R.[Hye-Ran],
Improved N-division output coding for multiclass learning problems,
ICPR04(III: 470-473).
IEEE DOI 0409
BibRef

Leang, P., Bhanu, B.,
Learning integrated perception-based speed control,
ICPR04(I: 813-816).
IEEE DOI 0409
BibRef

Taylor, G.W.,
A reinforcement learning framework for parameter control in computer vision applications,
CRV04(496-503).
IEEE DOI 0408
BibRef

Miasnikov, A.D., Rome, J.E., Haralick, R.M.,
A hierarchical projection pursuit clustering algorithm,
ICPR04(I: 268-271).
IEEE DOI 0409
BibRef

Shi, D., Ng, G.S., Gao, J., Yeung, D.S.,
Critical vector learning to construct RBF classifiers,
ICPR04(III: 359-362).
IEEE DOI 0409
BibRef

Eriksson, M.[Martin], Carlsson, S.[Stefan],
Qualitative Characterization and Use of Prior Information,
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,
CREST03(227-241). 0309
BibRef

Kubota, S., Mizutani, H., Kurosawa, Y.,
A discriminative learning criterion for the overall optimization of error and reject,
ICPR02(IV: 98-102).
IEEE DOI 0211
BibRef

Mizutani, H.[Hiroyuki],
Discriminative Learning for Minimum Error and Minimum Reject Classification,
ICPR98(Vol I: 136-140).
IEEE DOI 9808
BibRef

Seok, J.[Jinwuk], Lee, J.W.[Jeun-Woo],
The analysis of a stochastic differential approach for langevine comepetitive learning algorithm,
ICPR02(II: 80-83).
IEEE DOI 0211
BibRef

Naphade, M.R., Frey, B.J., Chen, L., Huang, T.S.,
Learning Sparse Multiple Cause Models,
ICPR00(Vol II: 642-647).
IEEE DOI 0009
BibRef

Hong, P., Huang, T.S.,
Learning to Extract Temporal Signal Patterns from Temporal Signal Sequence,
ICPR00(Vol II: 648-651).
IEEE DOI 0009
BibRef

Sato, A.[Atsushi],
A New Learning Formulation for Kernel Classifier Design,
ICPR10(2897-2900).
IEEE DOI 1008
BibRef

Sato, A.,
A Learning Method for Definite Canonicalization Based on Minimum Classification Error,
ICPR00(Vol II: 199-202).
IEEE DOI 0009
BibRef

Hansen, J.V., Heskes, T.,
General Bias/Variance Decomposition with Target Independent Variance of Error Functions Derived from the Exponential Family of Distributions,
ICPR00(Vol II: 207-210).
IEEE DOI 0009
BibRef

Jia, J., Abe, K.,
Clustering of Learning Images and Generation of Multiple Prototypes for Object Recognition,
MVA98(xx-yy). BibRef 9800

Kalkreuter, B.[Bjorn], Büker, U.[Ulrich],
Learning in an Active Hybrid Vision System,
ICPR98(Vol I: 178-181).
IEEE DOI 9808
BibRef

Shekhar, C.[Chandra], Burlina, P.[Philippe], Moisan, S.[Sabine],
Design of Self-Tuning IU Systems,
DARPA97(529-536). BibRef 9700

Takeuchi, Y.[Yutaka], Gros, P.[Patric], Hebert, M.[Martial], Ikeuchi, K.[Katsushi],
Visual Learning for Landmark Recognition,
DARPA97(1467-1474). BibRef 9700

Brooks, R.A., Grimson, W.E.L., Poggio, T., Koch, C., Sodini, C., Stein, L., Yang, W.,
A Trainable Modular Vision System,
DARPA97(1307-1314). BibRef 9700

Teller, A.[Astro], Veloso, M.[Manuela],
Evolutionary Learning for Orchestration of a Signal-to-Symbol Mapper,
DARPA97(1475-1482). BibRef 9700

Borga, M.[Magnus], Knutsson, H.[Hans], and Landelinus, T.[Tomas],
Learning Canonical Correlations,
SCIA97(xx-yy) 9705

HTML Version. BibRef

Chen, S.Y.[Shao-Yun], and Weng, J.[John],
On-Line Incremental Learning for Vision-Guided Real-Time Navigation Using Improved Updating,
SCIA97(xx-yy) 9705

HTML Version. BibRef
Earlier: A2, A1:
Incremental Learning for Vision-Based Navigation,
ICPR96(IV: 45-49).
IEEE DOI 9608
(Michigan State Univ., USA) BibRef

Chao, J., Nakayama, J.,
Cubical Singular Simplex Model for 3D Objects and Fast Computation of Homology Groups,
ICPR96(IV: 190-194).
IEEE DOI 9608
(Chuo Univ., J) BibRef

Zanardi, C., Herve, J.Y., Cohen, P.,
Mutual Learning of Unsupervised Interactions Between Mobile Robots,
ICPR96(IV: 40-44).
IEEE DOI 9608
(Ecole Polytechnique de Montreal, CDN) BibRef

Burge, M., Mayr, W., Burger, W.,
Recognition and Learning With Polymorphic Structural Components,
ICPR96(I: 19-23).
IEEE DOI 9608
(Johannes Kepler Univ., A) BibRef

Sajda, P., Spence, C.D., Pearson, J.D.,
Learning Image Context for Improved Computer-Aided Diagnosis,
ARPA96(1375-1380). BibRef 9600

Zheng, Y.J., Bhanu, B.,
Adaptive Object Detection Based on Modified Hebbian Learning,
ICPR96(IV: 164-168).
IEEE DOI 9608
BibRef
And:
Performance Improvement by Input Adaption Using Modified Hebbian Learning,
ARPA96(1381-1388). (Univ. of California, Riverside, USA) BibRef

Rong, S., Bhanu, B.,
Reinforcement Learning for Integrating Context with Clutter Models for Target Detection,
ARPA96(1389-1394). ATR. BibRef 9600

Narenthiran, N., Boult, T.E.,
Color Channel Mixing in Learning from Appearance,
ARPA96(1455-1456). BibRef 9600

Bhanu, B.[Bir], and Dutta, R.[Rabi],
A Learning System for Consolidated Recognition and Motion Analysis,
ARPA94(I:773-776). BibRef 9400

Khuller, S.[Samir], Rivlin, E.[Ehud], and Rosenfeld, A.[Azriel],
Learning to Navigate on a Graph,
ARPA94(I:789-795). BibRef 9400

Jovanovic, L.,
Learning Algorithm Based on Modified Structure of Pattern Classes,
ICPR92(I:487-490).
IEEE DOI BibRef 9200

Valev, V., Radeva, P.I.,
A method of solving pattern or image recognition problems by learning Boolean formulas,
ICPR92(II:359-362).
IEEE DOI 9208
BibRef

Soloway, E.M., Riseman, E.M.,
Levels of Pattern Description in Learning,
IJCAI77(801-811).Cp

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


Last update:Jul 15, 2017 at 20:56:55