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Principles of Nonparametric Learning

  • Book
  • © 2002

Overview

Editors:
  1. László Györfi

    1. Budapest University of Technology and Economics, Hungary

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

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Table of contents (6 chapters)

  1. Front Matter

    Pages N2-v
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  2. Pattern Classification and Learning Theory

    • G. Lugosi
    Pages 1-56

  3. Nonparametric Regression Estimation

    • L. Györfi, M. Kohler
    Pages 57-112

  4. Universal Prediction

    • N. Cesa-Bianchi
    Pages 113-162

  5. Learning-Theoretic Methods in Vector Quantization

    • T. Linder
    Pages 163-210

  6. Distribution and Density Estimation

    • L. Devroye, L. Györfi
    Pages 211-270

  7. Genetic Programming Applied to Model Identification

    • M. Sebag
    Pages 271-335
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Keywords

About this book

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.

Editors and Affiliations

  • Budapest University of Technology and Economics, Hungary

    László Györfi

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