Principles of Nonparametric Learning

Principles of Nonparametric Learning
Author: Laszlo Györfi
Publisher: Springer
Total Pages: 344
Release: 2014-05-04
Genre: Technology & Engineering
ISBN: 3709125685


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This volume provides a 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.


Principles of Nonparametric Learning
Language: en
Pages: 344
Authors: Laszlo Györfi
Categories: Technology & Engineering
Type: BOOK - Published: 2014-05-04 - Publisher: Springer

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This volume provides a systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and esti
Nonparametric and Semiparametric Models
Language: en
Pages: 317
Authors: Wolfgang Karl Härdle
Categories: Mathematics
Type: BOOK - Published: 2012-08-27 - Publisher: Springer Science & Business Media

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The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: T
Bayesian Nonparametrics
Language: en
Pages: 309
Authors: Nils Lid Hjort
Categories: Mathematics
Type: BOOK - Published: 2010-04-12 - Publisher: Cambridge University Press

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Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount
Nonparametric Measures of Association
Language: en
Pages: 108
Authors: Jean Dickinson Gibbons
Categories: Reference
Type: BOOK - Published: 1993-02-25 - Publisher: SAGE

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Aimed at helping the researcher select the most appropriate measure of association for two or more variables, the author clearly describes such techniques as Sp
Principles and Theory for Data Mining and Machine Learning
Language: en
Pages: 786
Authors: Bertrand Clarke
Categories: Computers
Type: BOOK - Published: 2009-07-21 - Publisher: Springer Science & Business Media

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Extensive treatment of the most up-to-date topics Provides the theory and concepts behind popular and emerging methods Range of topics drawn from Statistics, Co