Stock image for illustration purposes only - book cover, edition or condition may vary.
Lectures on the Nearest Neighbor Method
Luc Devroye
€ 178.89
FREE Delivery in Ireland
Description for Lectures on the Nearest Neighbor Method
Hardback. Series: Springer Series in the Data Sciences. Num Pages: 299 pages, 4 colour illustrations, 1 black & white tables, biography. BIC Classification: PBT; UFM; UYQM; UYQP. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 18. Weight in Grams: 613.
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. Gerard Biau is a professor at Universite 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. Gerard Biau is a professor at Universite Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal).
Product Details
Format
Hardback
Publication date
2015
Publisher
Springer International Publishing AG
Condition
New
Series
Springer Series in the Data Sciences
Number of Pages
290
Place of Publication
Cham, Switzerland
ISBN
9783319253862
SKU
V9783319253862
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
Reviews for Lectures on the Nearest Neighbor Method
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)