Overview
- Alexey Chervonenkis made an outstanding contribution to the areas of pattern recognition and computational learning
- Valuable for researchers and graduate students
- Contributors are leading scientists in statistics, theoretical computer science, and mathematics
- Includes supplementary material: sn.pub/extras
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Table of contents (25 chapters)
-
History of VC Theory
-
Reviews of Measures of Complexity
Keywords
- Algorithmic statistics
- Bayesian theory
- Causal inference
- Communicaton complexity
- Computational complexity
- Kernels
- Kolmogorov complexity
- Machine learning
- Metric entropy
- Optimization
- Overfitting
- Pattern recognition
- Statistical learning theory
- Supervised classification
- Support vector machines (SVMs);
- VC (Vapnik-Chervonenkis) dimension
About this book
This book brings together historical notes, reviews of research developments, fresh ideas on how to make VC (Vapnik–Chervonenkis) guarantees tighter, and new technical contributions in the areas of machine learning, statistical inference, classification, algorithmic statistics, and pattern recognition.
The contributors are leading scientists in domains such as statistics, mathematics, and theoretical computer science, and the book will be of interest to researchers and graduate students in these domains.
Editors and Affiliations
Bibliographic Information
Book Title: Measures of Complexity
Book Subtitle: Festschrift for Alexey Chervonenkis
Editors: Vladimir Vovk, Harris Papadopoulos, Alexander Gammerman
DOI: https://doi.org/10.1007/978-3-319-21852-6
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Hardcover ISBN: 978-3-319-21851-9Published: 14 September 2015
Softcover ISBN: 978-3-319-35778-2Published: 22 October 2016
eBook ISBN: 978-3-319-21852-6Published: 03 September 2015
Edition Number: 1
Number of Pages: XXXI, 399
Number of Illustrations: 17 b/w illustrations, 30 illustrations in colour
Topics: Artificial Intelligence, Statistical Theory and Methods, Probability and Statistics in Computer Science, Optimization