Mimicking Learning for 1-NN Classifiers

Przemysław Śliwiński, Paweł Wachel, Jerzy W. Rozenblit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider the problem of mimicking the behavior of the nearest neighbor algorithm with an unknown distance measure. Our goal is, in particular, to design and update a learning set so that two NN algorithms with various distance functions ρp and ρq, 0 < p, q< ∞, classify in the same way, and to approximate the behavior of one classifier by the other. The autism disorder-related motivation of the problem is presented.

Original languageEnglish (US)
Title of host publicationComputational Science – ICCS 2021 - 21st International Conference, Proceedings
EditorsMaciej Paszynski, Dieter Kranzlmüller, Dieter Kranzlmüller, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M.A. Sloot, Peter M.A. Sloot, Peter M.A. Sloot
PublisherSpringer Science and Business Media Deutschland GmbH
Pages75-80
Number of pages6
ISBN (Print)9783030779665
DOIs
StatePublished - 2021
Event21st International Conference on Computational Science, ICCS 2021 - Virtual, Online
Duration: Jun 16 2021Jun 18 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12744 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Computational Science, ICCS 2021
CityVirtual, Online
Period6/16/216/18/21

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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