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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Schölkopf, Bernhard (Editor ), Luo, Zhiyuan (Editor ), Vovk, Vladimir (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Temas:
Artificial intelligence.
Statistics .
Computer science-Mathematics.
Mathematical statistics.
Mathematical optimization.
Artificial Intelligence.
Statistical Theory and Methods.
Probability and Statistics in Computer Science.
Optimization.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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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