Classification with positive and negative equivalence constraints: Theory, computation and human experiments

Rubi Hammer, Tomer Hertz, Shaul Hochstein, Daphna Weinshall

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

8 Scopus citations

Abstract

We tested the efficiency of category learning when participants are provided only with pairs of objects, known to belong either to the same class (Positive Equivalence Constraints or PECs) or to different classes (Negative Equivalence Constraints or NECs). Our results in a series of cognitive experiments show dramatic differences in the usability of these two information building blocks, even when they are chosen to contain the same amount of information. Specifically, PECs seem to be used intuitively and quite efficiently, while people are rarely able to gain much information from NECs (unless they are specifically directed for the best way of using them). Tests with a constrained EM clustering algorithm under similar conditions also show superior performance with PECs. We conclude with a theoretical analysis, showing (by analogy to graph cut problems) that the satisfaction of NECs is computationally intractable, whereas the satisfaction of PECs is straightforward. Furthermore, we show that PECs convey more information than NECs by relating their information content to the number of different graph colorings. These inherent differences between PECs and NECs may explain why people readily use PECs, while many of them need specific directions to be able to use NECs effectively.

Original languageEnglish
Title of host publicationAdvances in Brain, Vision, and Artificial Intelligence - Second International Symposium, BVAI 2007, Proceedings
Pages264-276
Number of pages13
DOIs
StatePublished - 1 Dec 2007
Externally publishedYes
Event2nd International Symposium on Brain, Vision, and Artificial Intelligence, BVAI 2007 - Naples, Italy
Duration: 10 Oct 200712 Oct 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4729 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Symposium on Brain, Vision, and Artificial Intelligence, BVAI 2007
Country/TerritoryItaly
CityNaples
Period10/10/0712/10/07

Keywords

  • Categorization
  • Expectation Maximization
  • Rule learning
  • Similarity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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