[(Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik )] [Author: Bernhard Scholkopf] [Jan-2014]

by Bernhard Schölkopf

Part I - History of Statistical Learning Theory.- Chap. 1 - In Doklady Akademii Nauk SSSR, 181(4), 1968.- Chap. 2 - On the Uniform Convergence of the Frequencies of Occurrence of Events to Their Probabilities.- Chap. 3 - Early History of Support Vector Machines.- Part II - Theory and Practice of Statistical Learning Theory.- Chap. 4 - Some Remarks on the Statistical Analysis of SVMs and Related Methods.- Chap. 5 - Explaining AdaBoost.- Chap. 6 - On the Relations and Differences Between Popper Dimension, Exclusion Dimension and VC-Dimension.- Chap. 7 - On Learnability, Complexity and Stability.- Chap. 8 - Loss Functions.- Chap. 9 - Statistical Learning Theory in Practice.- Chap. 10 - PAC-Bayesian Theory.- Chap. 11 - Kernel Ridge Regression.- Chap. 12 - Multi-task Learning for Computational Overview and Outlook.- Chap. 13 - Semi-supervised Learning in Causal and Anticausal Settings.- Chap. 14 - Strong Universal Consistent Estimate of the Minimum Mean-Squared Error.- Chap. 15 - The Median Hypothesis.- Chap. 16 - Efficient Transductive Online Learning via Randomized Rounding.- Chap. 17 - Pivotal Estimation in High-Dimensional Regression via Linear Programming.- Chap. 18 - Some Observations on Sparsity Inducing Regularization Methods for Machine Learning.- Chap. 19 - Sharp Oracle Inequalities in Low Rank Estimation.- Chap. 20 - On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel-Based Methods.- Chap. 21 - Kernels, Pre-images and Optimization.- Chap. 22 - Efficient Learning of Sparse Ranking Functions.- Chap. 23 - Direct Approximation of Divergences Between Probability Distributions.- Index.

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First published December 2, 2013

About the author

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.