Empirical Inference Festschrift in Honor of Vladimir N. Vapnik /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Schölkopf, Bernhard (Επιμελητής έκδοσης), Luo, Zhiyuan (Επιμελητής έκδοσης), Vovk, Vladimir (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
• Θέματα: Computer science.; Mathematical statistics.; Artificial intelligence.; Mathematical optimization.; Statistics.; Computer Science.; Artificial Intelligence (incl. Robotics).; Statistical Theory and Methods.; Probability and Statistics in Computer Science.; Optimization.

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...

Πίνακας περιεχομένων:

• 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.