Springer the Data Sciences

By: GÃ©rard Biau, Luc Devroye

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Hardcover | 15 December 2015

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Hardcover
302 Pages

Dimensions(cm)
23.5 x 15.88 x 1.91

Hardcover

RRP $219.00

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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.

G©rard Biau is a professor at Universit© Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal).

Industry Reviews

"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)

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