Principles of Nonparametric Learning

  • László Györfi

Part of the International Centre for Mechanical Sciences book series (CISM, volume 434)

Table of contents

  1. Front Matter
    Pages N2-v
  2. L. Györfi, M. Kohler
    Pages 57-112
  3. N. Cesa-Bianchi
    Pages 113-162
  4. L. Devroye, L. Györfi
    Pages 211-270

About this book

Introduction

The book provides systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density estimation and genetic programming. The book is mainly addressed to postgraduates in engineering, mathematics, computer science, and researchers in universities and research institutions.

Keywords

Pattern Recognition Probability and Statistics in Computer Science Signal Processing Statistical Theory and Methods algorithms classification cognition complexity computer science genetic programming grammar kernel learning pattern pattern recognition probability programming signal processing statistics

Editors and affiliations

  • László Györfi
    • 1
  1. 1.Budapest University of Technology and EconomicsHungary

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-7091-2568-7
  • Copyright Information CISM Udine 2002
  • Publisher Name Springer, Vienna
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-211-83688-0
  • Online ISBN 978-3-7091-2568-7
  • Series Print ISSN 0254-1971
  • Series Online ISSN 2309-3706
  • About this book