Nearest-Neighbor Methods: A Modern Perspective

Aryeh Kontorovich; Samory Kpotufe

doi:10.1007/978-3-031-24628-9_5

Nearest-Neighbor Methods: A Modern Perspective

Aryeh Kontorovich
, Samory Kpotufe

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

3 Scopus citations

Abstract

This chapter aims at providing an overview of various modern approaches to learning with nearest neighbors in general metric spaces. We provide the necessary background and then proceed to cover classification, regression—with sufficient detail and literature pointers to yield practical insights into how various configuration and pre-processing choices, e.g., metric, the number of neighbors, data subsampling, and compression, affect learning and computational performance.

Original language	English
Title of host publication	Machine Learning for Data Science Handbook
Subtitle of host publication	Data Mining and Knowledge Discovery Handbook, Third Edition
Publisher	Springer International Publishing
Pages	75-92
Number of pages	18
ISBN (Electronic)	9783031246289
ISBN (Print)	9783031246272
DOIs	https://doi.org/10.1007/978-3-031-24628-9_5
State	Published - 1 Jan 2023

ASJC Scopus subject areas

General Computer Science
General Mathematics

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10.1007/978-3-031-24628-9_5

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Nearest-Neighbor Methods: A Modern Perspective

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ASJC Scopus subject areas

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