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...
Uloženo v:
| Další autoři: | |
|---|---|
| Korporace: | |
| Médium: | E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
Berlin, Heidelberg :
Springer Berlin Heidelberg :
2013
|
| Žánr/forma: | elektronické knihy |
| ISBN: | 9783642411366 |
| On-line přístup: | Plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo otaguje tento záznam!
|
Obsah:
- 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