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Lectures on the Nearest Neighbor Method de Gérard Biau

Lectures on the Nearest Neighbor Method: Springer Series in the Data Sciences

Autor Gérard Biau, Luc Devroye
en Limba Engleză Hardback – 15 dec 2015
This text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinatorial and geometric ideas for understanding, analyzing and developing nearest neighbor methods.
Gérard Biau is a professor at Université Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal).   
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Specificații

ISBN-13: 9783319253862
ISBN-10: 3319253867
Pagini: 290
Ilustrații: IX, 290 p. 4 illus. in color.
Dimensiuni: 155 x 235 x 25 mm
Greutate: 0.62 kg
Ediția:1st ed. 2015
Editura: Springer International Publishing
Colecția Springer
Seria Springer Series in the Data Sciences

Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

Part I: Density Estimation.- Order Statistics and Nearest Neighbors.- The Expected Nearest Neighbor Distance.- The k-nearest Neighbor Density Estimate.- Uniform Consistency.- Weighted k-nearest neighbor density estimates.- Local Behavior.- Entropy Estimation.- Part II: Regression Estimation.- The Nearest Neighbor Regression Function Estimate.- The 1-nearest Neighbor Regression Function Estimate.- LP-consistency and Stone's Theorem.- Pointwise Consistency.- Uniform Consistency.- Advanced Properties of Uniform Order Statistics.- Rates of Convergence.- Regression: The Noisless Case.- The Choice of a Nearest Neighbor Estimate.- Part III: Supervised Classification.- Basics of Classification.- The 1-nearest Neighbor Classification Rule.- The Nearest Neighbor Classification Rule. Appendix.- Index.

Recenzii

“This book deals with different aspects regarding this approach, starting with the standard k-nearest neighbor model, and passing through the weighted k-nearest neighbor model, estimations for entropy, regression functions etc. … It is intended for a large audience, including students, teachers, and researchers.” (Florin Gorunescu, zbMATH 1330.68001, 2016)

Textul de pe ultima copertă

This text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinatorial and geometric ideas for understanding, analyzing and developing nearest neighbor methods.
Gérard Biau is a professor at Université Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal).   

Caracteristici

Presents a rigorous overview of nearest neighbor methods Many different components covered: statistical, probabilistic, combinatorial, and geometric ideas Extensive appendix material provided