Measures of Complexity

Festschrift for Alexey Chervonenkis

  • Vladimir Vovk
  • Harris Papadopoulos
  • Alexander Gammerman

Table of contents

  1. Front Matter
    Pages i-xxxi
  2. History of VC Theory

  3. Reviews of Measures of Complexity

  4. Making VC Bounds More Accurate

  5. Advances in VC Theory

    1. Front Matter
      Pages 167-169

About this book

Introduction

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.

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

Editors and affiliations

  • Vladimir Vovk
    • 1
  • Harris Papadopoulos
    • 2
  • Alexander Gammerman
    • 3
  1. 1.Dept. of Computer ScienceRoyal Holloway, Univ of LondonEghamUnited Kingdom
  2. 2.Frederick UniversityNicosiaCyprus
  3. 3.Dept. of Computer ScienceUniversity of LondonEghamUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-21852-6
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-21851-9
  • Online ISBN 978-3-319-21852-6
  • About this book
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