Lectures on the Nearest Neighbor Method
1st Edition
Published on 21. March 2019
Book
Paperback/Softback
IX, 290 pages
978-3-319-79782-3 (ISBN)
Description
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).
Reviews / Votes
"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)More details
Product info
Previously published in hardcover
Series
Edition
Softcover reprint of the original 1st ed. 2015
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
4
1 s/w Tabelle, 4 farbige Abbildungen
IX, 290 p. 4 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 17 mm
Weight
458 gr
ISBN-13
978-3-319-79782-3 (9783319797823)
DOI
10.1007/978-3-319-25388-6
Schweitzer Classification
Other editions
Additional editions
Gérard Biau | Luc Devroye
Lectures on the Nearest Neighbor Method
Book
12/2015
Springer
€149.79
Shipment within 10-15 days
Content
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.