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Devroye / Biau

Lectures on the Nearest Neighbor Method

Medium: Buch
ISBN: 978-3-319-25386-2
Verlag: Springer International Publishing
Erscheinungstermin: 15.12.2015
Lieferfrist: bis zu 10 Tage

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).   


Produkteigenschaften


  • Artikelnummer: 9783319253862
  • Medium: Buch
  • ISBN: 978-3-319-25386-2
  • Verlag: Springer International Publishing
  • Erscheinungstermin: 15.12.2015
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2015
  • Serie: Springer Series in the Data Sciences
  • Produktform: Gebunden
  • Gewicht: 5738 g
  • Seiten: 290
  • Format (B x H x T): 160 x 241 x 22 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

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.