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Lectures on the Nearest Neighbor Method
Buch von Luc Devroye (u. a.)
Sprache: Englisch

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Beschreibung
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).
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).
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2015
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: Springer Series in the Data Sciences
Inhalt: ix
290 S.
4 farbige Illustr.
290 p. 4 illus. in color.
ISBN-13: 9783319253862
ISBN-10: 3319253867
Sprache: Englisch
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Devroye, Luc
Biau, Gérard
Auflage: 1st ed. 2015
Hersteller: Springer Nature Switzerland
Springer International Publishing
Springer International Publishing AG
Springer Series in the Data Sciences
Maße: 241 x 160 x 22 mm
Von/Mit: Luc Devroye (u. a.)
Erscheinungsdatum: 15.12.2015
Gewicht: 0,617 kg
Artikel-ID: 104211530
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2015
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: Springer Series in the Data Sciences
Inhalt: ix
290 S.
4 farbige Illustr.
290 p. 4 illus. in color.
ISBN-13: 9783319253862
ISBN-10: 3319253867
Sprache: Englisch
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Devroye, Luc
Biau, Gérard
Auflage: 1st ed. 2015
Hersteller: Springer Nature Switzerland
Springer International Publishing
Springer International Publishing AG
Springer Series in the Data Sciences
Maße: 241 x 160 x 22 mm
Von/Mit: Luc Devroye (u. a.)
Erscheinungsdatum: 15.12.2015
Gewicht: 0,617 kg
Artikel-ID: 104211530
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