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Lectures on the Nearest Neighbor Method Softcover reprint of the original 1st ed. 2015 [Pehme köide]

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  • Formaat: Paperback / softback, 290 pages, kõrgus x laius: 235x155 mm, kaal: 462 g, 4 Illustrations, color; IX, 290 p. 4 illus. in color., 1 Paperback / softback
  • Sari: Springer Series in the Data Sciences
  • Ilmumisaeg: 21-Mar-2019
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319797824
  • ISBN-13: 9783319797823
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Lectures on the Nearest Neighbor Method Softcover reprint of the original 1st ed. 2015Suurem pilt
  • Formaat: Paperback / softback, 290 pages, kõrgus x laius: 235x155 mm, kaal: 462 g, 4 Illustrations, color; IX, 290 p. 4 illus. in color., 1 Paperback / softback
  • Sari: Springer Series in the Data Sciences
  • Ilmumisaeg: 21-Mar-2019
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319797824
  • ISBN-13: 9783319797823
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319797823.html

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).   

Arvustused

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)

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.