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Empirical Inference Festschrift in Honor of Vladimir N. Vapnik /

This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the...

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Corporate Author: SpringerLink (Online service)
Other Authors: Schölkopf, Bernhard. (Editor, http://id.loc.gov/vocabulary/relators/edt), Luo, Zhiyuan. (Editor, http://id.loc.gov/vocabulary/relators/edt), Vovk, Vladimir. (Editor, http://id.loc.gov/vocabulary/relators/edt)
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Edition:1st ed. 2013.
Subjects:
Artificial intelligence.
Statistics .
Mathematical statistics.
Mathematical optimization.
Artificial Intelligence.
Statistical Theory and Methods.
Probability and Statistics in Computer Science.
Optimization.
Online Access:https://doi.org/10.1007/978-3-642-41136-6
Table of Contents:
  • Part I - History of Statistical Learning Theory
  • Chap. 1 - In Hindsight: 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 Biology: 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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