Principles of Nonparametric Learning

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

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.

• 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

• Budapest University of Technology and Economics, Hungary

  László Györfi

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