An incremental nearest neighbor algorithm with queries

Joel Ratsaby

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We consider the general problem of learning multi-category classification from labeled examples. We present experimental results for a nearest neighbor algorithm which actively selects samples from different pattern classes according to a querying rule instead of the a priori class probabilities. The amount of improvement of this query-based approach over the passive batch approach depends on the complexity of the Bayes rule. The principle on which this algorithm is based is general enough to be used in any learning algorithm which permits a model-selection criterion and for which the error rate of the classifier is calculable in terms of the complexity of the model.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 10 - Proceedings of the 1997 Conference, NIPS 1997
PublisherNeural information processing systems foundation
Pages612-618
Number of pages7
ISBN (Print)0262100762, 9780262100762
StatePublished - 1 Jan 1998
Externally publishedYes
Event11th Annual Conference on Neural Information Processing Systems, NIPS 1997 - Denver, CO, United States
Duration: 1 Dec 19976 Dec 1997

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Conference

Conference11th Annual Conference on Neural Information Processing Systems, NIPS 1997
Country/TerritoryUnited States
CityDenver, CO
Period1/12/976/12/97

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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