The last few years have witnessed important new developments in the theory and practice of pattern classification. We intend to survey some of the main new ideas that have led to these recent results.
Mots-clés : pattern recognition, statistical learning theory, concentration inequalities, empirical processes, model selection
@article{PS_2005__9__323_0, author = {Boucheron, St\'ephane and Bousquet, Olivier and Lugosi, G\'abor}, title = {Theory of classification : a survey of some recent advances}, journal = {ESAIM: Probability and Statistics}, pages = {323--375}, publisher = {EDP-Sciences}, volume = {9}, year = {2005}, doi = {10.1051/ps:2005018}, mrnumber = {2182250}, zbl = {1136.62355}, language = {en}, url = {https://www.numdam.org/articles/10.1051/ps:2005018/} }
TY - JOUR AU - Boucheron, Stéphane AU - Bousquet, Olivier AU - Lugosi, Gábor TI - Theory of classification : a survey of some recent advances JO - ESAIM: Probability and Statistics PY - 2005 SP - 323 EP - 375 VL - 9 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ps:2005018/ DO - 10.1051/ps:2005018 LA - en ID - PS_2005__9__323_0 ER -
%0 Journal Article %A Boucheron, Stéphane %A Bousquet, Olivier %A Lugosi, Gábor %T Theory of classification : a survey of some recent advances %J ESAIM: Probability and Statistics %D 2005 %P 323-375 %V 9 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ps:2005018/ %R 10.1051/ps:2005018 %G en %F PS_2005__9__323_0
Boucheron, Stéphane; Bousquet, Olivier; Lugosi, Gábor. Theory of classification : a survey of some recent advances. ESAIM: Probability and Statistics, Tome 9 (2005), pp. 323-375. doi : 10.1051/ps:2005018. https://www.numdam.org/articles/10.1051/ps:2005018/
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- On signal representations within the Bayes decision framework, Pattern Recognition, Volume 45 (2012) no. 5, p. 1853 | DOI:10.1016/j.patcog.2011.11.015
- Risk bounds for CART classifiers under a margin condition, Pattern Recognition, Volume 45 (2012) no. 9, p. 3523 | DOI:10.1016/j.patcog.2012.02.021
- Kullback–Leibler aggregation and misspecified generalized linear models, The Annals of Statistics, Volume 40 (2012) no. 2 | DOI:10.1214/11-aos961
- Optimal weighted nearest neighbour classifiers, The Annals of Statistics, Volume 40 (2012) no. 5 | DOI:10.1214/12-aos1049
- Generalization Bounds, Encyclopedia of Machine Learning (2011), p. 447 | DOI:10.1007/978-0-387-30164-8_328
- Regularization, Encyclopedia of Machine Learning (2011), p. 845 | DOI:10.1007/978-0-387-30164-8_712
- Supervised Learning by Support Vector Machines, Handbook of Mathematical Methods in Imaging (2011), p. 959 | DOI:10.1007/978-0-387-92920-0_22
- Statistical Learning Theory: Models, Concepts, and Results, Inductive Logic, Volume 10 (2011), p. 651 | DOI:10.1016/b978-0-444-52936-7.50016-1
- Adaptive partitioning schemes for bipartite ranking, Machine Learning, Volume 83 (2011) no. 1, p. 31 | DOI:10.1007/s10994-010-5190-y
- Learning noisy linear classifiers via adaptive and selective sampling, Machine Learning, Volume 83 (2011) no. 1, p. 71 | DOI:10.1007/s10994-010-5191-x
- A combinatorial approach to hypothesis similarity in generalization bounds, Pattern Recognition and Image Analysis, Volume 21 (2011) no. 4, p. 616 | DOI:10.1134/s1054661811040109
- Tight Combinatorial Generalization Bounds for Threshold Conjunction Rules, Pattern Recognition and Machine Intelligence, Volume 6744 (2011), p. 66 | DOI:10.1007/978-3-642-21786-9_13
- A high-dimensional Wilks phenomenon, Probability Theory and Related Fields, Volume 150 (2011) no. 3-4, p. 405 | DOI:10.1007/s00440-010-0278-7
- , 19th International Symposium in Robot and Human Interactive Communication (2010), p. 165 | DOI:10.1109/roman.2010.5598664
- Sample Complexity of Classifiers Taking Values in ℝQ, Application to Multi-Class SVMs, Communications in Statistics - Theory and Methods, Volume 39 (2010) no. 3, p. 543 | DOI:10.1080/03610920903140288
- Maxisets for Model Selection, Constructive Approximation, Volume 31 (2010) no. 2, p. 195 | DOI:10.1007/s00365-009-9062-2
- Some Properties of Gaussian Reproducing Kernel Hilbert Spaces and Their Implications for Function Approximation and Learning Theory, Constructive Approximation, Volume 32 (2010) no. 2, p. 307 | DOI:10.1007/s00365-009-9080-0
- Overlaying Classifiers: A Practical Approach to Optimal Scoring, Constructive Approximation, Volume 32 (2010) no. 3, p. 619 | DOI:10.1007/s00365-010-9084-9
- Plugin procedure in segmentation and application to hyperspectral image segmentation, Electronic Journal of Statistics, Volume 4 (2010) no. none | DOI:10.1214/10-ejs567
- Adaptive Kernel Methods Using the Balancing Principle, Foundations of Computational Mathematics, Volume 10 (2010) no. 4, p. 455 | DOI:10.1007/s10208-010-9064-2
- A discriminative model for semi-supervised learning, Journal of the ACM, Volume 57 (2010) no. 3, p. 1 | DOI:10.1145/1706591.1706599
- Combinatorial shell bounds for generalization ability, Pattern Recognition and Image Analysis, Volume 20 (2010) no. 4, p. 459 | DOI:10.1134/s1054661810040061
- On Convergence of Kernel Learning Estimators, SIAM Journal on Optimization, Volume 20 (2010) no. 3, p. 1205 | DOI:10.1137/070696817
- A survey of cross-validation procedures for model selection, Statistics Surveys, Volume 4 (2010) no. none | DOI:10.1214/09-ss054
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- Adaptive Estimation of the Optimal ROC Curve and a Bipartite Ranking Algorithm, Algorithmic Learning Theory, Volume 5809 (2009), p. 216 | DOI:10.1007/978-3-642-04414-4_20
- Efficiency of classification methods based on empirical risk minimization, Cybernetics and Systems Analysis, Volume 45 (2009) no. 5, p. 750 | DOI:10.1007/s10559-009-9153-x
- Model Selection, Handbook of Financial Time Series (2009), p. 889 | DOI:10.1007/978-3-540-71297-8_39
- Agnostic active learning, Journal of Computer and System Sciences, Volume 75 (2009) no. 1, p. 78 | DOI:10.1016/j.jcss.2008.07.003
- Instability, complexity, and evolution, Journal of Mathematical Sciences, Volume 158 (2009) no. 6, p. 787 | DOI:10.1007/s10958-009-9412-4
- Fast learning rates in statistical inference through aggregation, The Annals of Statistics, Volume 37 (2009) no. 4 | DOI:10.1214/08-aos623
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- HECTAR: A method to predict subcellular targeting in heterokonts, BMC Bioinformatics, Volume 9 (2008) no. 1 | DOI:10.1186/1471-2105-9-393
- Bayesian approach, theory of empirical risk minimization. Comparative analysis, Cybernetics and Systems Analysis, Volume 44 (2008) no. 6, p. 822 | DOI:10.1007/s10559-008-9058-0
- Classification with minimax fast rates for classes of Bayes rules with sparse representation, Electronic Journal of Statistics, Volume 2 (2008) no. none | DOI:10.1214/07-ejs015
- Lower Bounds for the Empirical Minimization Algorithm, IEEE Transactions on Information Theory, Volume 54 (2008) no. 8, p. 3797 | DOI:10.1109/tit.2008.926323
- Obtaining fast error rates in nonconvex situations, Journal of Complexity, Volume 24 (2008) no. 3, p. 380 | DOI:10.1016/j.jco.2007.09.001
- Reducing mechanism design to algorithm design via machine learning, Journal of Computer and System Sciences, Volume 74 (2008) no. 8, p. 1245 | DOI:10.1016/j.jcss.2007.08.002
- PAC-Bayesian bounds for randomized empirical risk minimizers, Mathematical Methods of Statistics, Volume 17 (2008) no. 4, p. 279 | DOI:10.3103/s1066530708040017
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- Constructing processes with prescribed mixing coefficients, Statistics Probability Letters, Volume 78 (2008) no. 17, p. 2910 | DOI:10.1016/j.spl.2008.04.016
- Ranking and Empirical Minimization of U-statistics, The Annals of Statistics, Volume 36 (2008) no. 2 | DOI:10.1214/009052607000000910
- Kernel methods in machine learning, The Annals of Statistics, Volume 36 (2008) no. 3 | DOI:10.1214/009053607000000677
- Learning by mirror averaging, The Annals of Statistics, Volume 36 (2008) no. 5 | DOI:10.1214/07-aos546
- Optimal rates of aggregation in classification under low noise assumption, Bernoulli, Volume 13 (2007) no. 4 | DOI:10.3150/07-bej6044
- Multi-kernel regularized classifiers, Journal of Complexity, Volume 23 (2007) no. 1, p. 108 | DOI:10.1016/j.jco.2006.06.007
- On regularization algorithms in learning theory, Journal of Complexity, Volume 23 (2007) no. 1, p. 52 | DOI:10.1016/j.jco.2006.07.001
- Suboptimality of Penalized Empirical Risk Minimization in Classification, Learning Theory, Volume 4539 (2007), p. 142 | DOI:10.1007/978-3-540-72927-3_12
- Guest editorial: Learning theory, Machine Learning, Volume 66 (2007) no. 2-3, p. 115 | DOI:10.1007/s10994-007-0753-2
- Generalized mirror averaging and D-convex aggregation, Mathematical Methods of Statistics, Volume 16 (2007) no. 3, p. 246 | DOI:10.3103/s1066530707030040
- Fast learning rates for plug-in classifiers, The Annals of Statistics, Volume 35 (2007) no. 2 | DOI:10.1214/009053606000001217
- Simultaneous adaptation to the margin and to complexity in classification, The Annals of Statistics, Volume 35 (2007) no. 4 | DOI:10.1214/009053607000000055
- On the Kernel Rule for Function Classification, Annals of the Institute of Statistical Mathematics, Volume 58 (2006) no. 3, p. 619 | DOI:10.1007/s10463-006-0032-1
- Classification with reject option, Canadian Journal of Statistics, Volume 34 (2006) no. 4, p. 709 | DOI:10.1002/cjs.5550340410
- Optimal Oracle Inequality for Aggregation of Classifiers Under Low Noise Condition, Learning Theory, Volume 4005 (2006), p. 364 | DOI:10.1007/11776420_28
- Functional Classification with Margin Conditions, Learning Theory, Volume 4005 (2006), p. 94 | DOI:10.1007/11776420_10
- Categorization, SSRN Electronic Journal (2006) | DOI:10.2139/ssrn.884232
- Statistical inference on graphs, Statistics Decisions, Volume 24 (2006) no. 2, p. 209 | DOI:10.1524/stnd.2006.24.2.209
- Oracle inequalities for multi-fold cross validation, Statistics Decisions, Volume 24 (2006) no. 3, p. 351 | DOI:10.1524/stnd.2006.24.3.351
- Functional Classification in Hilbert Spaces, IEEE Transactions on Information Theory, Volume 51 (2005) no. 6, p. 2163 | DOI:10.1109/tit.2005.847705
- Ranking and Scoring Using Empirical Risk Minimization, Learning Theory, Volume 3559 (2005), p. 1 | DOI:10.1007/11503415_1
- A PAC-Style Model for Learning from Labeled and Unlabeled Data, Learning Theory, Volume 3559 (2005), p. 111 | DOI:10.1007/11503415_8
- The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets, Machine Learning and Data Mining in Pattern Recognition, Volume 3587 (2005), p. 90 | DOI:10.1007/11510888_10
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