Lectures on the nearest neighbor method
type: Item
instance of: scholarly article
title: Lectures on the nearest neighbor method
zbMATH Open document ID: 1330.68001
DOI: 10.1007/978-3-319-25388-6
published in: Springer Series in the Data Sciences
publication date: 2015-11-12T00:00:00Z
review text: The nearest neighbor paradigm is one of the most used machine learning methods in the classification domain. 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. Problems like point-wise and uniform consistency, rates of convergence, noiseless case etc. are also approached. The last part of the book deals with the supervised classification via \(k\)-nearest neighbors for fixed or variable parameter \(k\). The book ends with an appendix. It is intended for a large audience, including students, teachers, and researchers.
reviewed by: Florin Gorunescu
Mathematics Subject Classification ID: 68-01
Mathematics Subject Classification ID: 68-02
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68T10
Mathematics Subject Classification ID: 62P30
zbMATH DE Number: 6507957
zbMATH Keywords: order statistics
zbMATH Keywords: nearest neighbors
zbMATH Keywords: \(k\)-nearest neighbor density
zbMATH Keywords: uniform consistency
zbMATH Keywords: weighted \(k\)-nearest neighbor density
zbMATH Keywords: local behavior
zbMATH Keywords: entropy
zbMATH Keywords: regression estimation
zbMATH Keywords: rates of convergence
zbMATH Keywords: supervised classification, nearest neighbor rules
author: Gérard Biau
author: Luc Devroye
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1007/978-3-319-25388-6
OpenAlex ID: W2303810353
description: scientific article; zbMATH DE number 6507957