Lectures on the Nearest Neighbor Method

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, analyzi...

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Main Author: Biau, Gérard.
Corporate Author: SpringerLink (Online service)
Other Authors: Devroye, Luc.
Format: Electronic
Language: English
Published: Cham : Springer International Publishing : 2015.
Edition: 1st ed. 2015.
Series: Springer Series in the Data Sciences,
Subjects:
Online Access: http://dx.doi.org/10.1007/978-3-319-25388-6
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100 1 |a Biau, Gérard.  |e author. 
245 1 0 |a Lectures on the Nearest Neighbor Method  |h [electronic resource] /  |c by Gérard Biau, Luc Devroye. 
250 |a 1st ed. 2015. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2015. 
300 |a IX, 290 p. 4 illus. in color.  |b online resource. 
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490 1 |a Springer Series in the Data Sciences,  |x 2365-5674 
505 0 |a 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. 
520 |a 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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700 1 |a Devroye, Luc.  |e author. 
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830 0 |a Springer Series in the Data Sciences,  |x 2365-5674 
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