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＜電子ブック＞
Lectures on the Nearest Neighbor Method / by Gérard Biau, Luc Devroye
(Springer Series in the Data Sciences. ISSN:23655682)

版	1st ed. 2015.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2015
大きさ	IX, 290 p. 4 illus. in color : online resource
著者標目	*Biau, Gérard author Devroye, Luc author SpringerLink (Online service)
件　名	LCSH:Probabilities LCSH:Pattern recognition systems LCSH:Mathematical statistics—Data processing FREE:Probability Theory FREE:Automated Pattern Recognition FREE:Statistics and Computing
一般注記	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 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).    HTTP:URL=https://doi.org/10.1007/978-3-319-25388-6

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