Cantitate/Preț
Produs
Total produse
Vezi coșul
Lectures on the Nearest Neighbor Method de Gérard Biau

Lectures on the Nearest Neighbor Method

Autor Gérard Biau, Luc Devroye
en Limba Engleză Hardback – 15 dec 2015
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).   
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 85582 lei  6-8 săpt.
  Springer International Publishing – 21 mar 2019 85582 lei  6-8 săpt.
Hardback (1) 86219 lei  6-8 săpt.
  Springer – 15 dec 2015 86219 lei  6-8 săpt.

Preț: 86219 lei

Preț vechi: 105146 lei
-18% Nou

Puncte Express: 1293
Preț estimativ în valută:
15257€ • 17890$ • 13399£

Carte tipărită la comandă

Livrare economică 06-20 februarie 26

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783319253862
ISBN-10: 3319253867
Pagini: 300
Ilustrații: IX, 290 p. 4 illus. in color.
Dimensiuni: 160 x 241 x 22 mm
Greutate: 0.62 kg
Ediția:1st edition 2015
Editura: Springer
Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

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.

Recenzii

“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)

Textul de pe ultima copertă

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).   

Caracteristici

Presents a rigorous overview of nearest neighbor methods Many different components covered: statistical, probabilistic, combinatorial, and geometric ideas Extensive appendix material provided