Open problems in geometric methods for instance-based learning

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In the typical approach to instance-based learning, random data (the training set of patterns) are collected and used to design a decision rule (classifier). One of the most well known such rules is the k-nearest-neighbor decision rule in which an unknown pattern is classified into the majority class among its k nearest neighbors in the training set. In the past fifty years many approaches have been proposed to improve the performance of this rule. More recently geometric methods have been found to be the best. Here we mention a variety of open problems of a computational geometric nature that arize in these methods. To provide some context and motivation for these open problems we briefly describe the methods and list some key references.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsJin Akiyama, Mikio Kano
PublisherSpringer Verlag
Pages273-283
Number of pages11
ISBN (Print)3540207767, 9783540207764
DOIs
StatePublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2866
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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