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Principles of Nonparametric Learning|Laszlo Györfi

Principles of Nonparametric Learning

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Overview

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.

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Details

  • ISBN-13: 9783211836880
  • ISBN-10: 3211836888
  • Publisher: Springer
  • Publish Date: July 2002
  • Dimensions: 9.61 x 6.69 x 0.73 inches
  • Shipping Weight: 1.23 pounds
  • Page Count: 335

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