Forgetting and aging of knowledge in concept formation

Miroslav Kubat, Ivana Krizakova

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

One of the problems solved by machine learning based techniques is symbolic data analysis and concept formation. We report on the program FAVORIT, which achieves a performance improvement over its predecessors (such as UNIMEM) by means of a simple mechanism mimicking the shortcomings of human learning: aging of knowledge and forgetting. When applied to large and noisy data sets, these characteristics enable efficient restructuring and pruning of the internal knowledge structures. The paper contains a brief description of the program, together with the rationale behind its philosophy, as well as a simple case study.

Original languageEnglish (US)
Pages (from-to)195-206
Number of pages12
JournalApplied Artificial Intelligence
Volume6
Issue number2
DOIs
StatePublished - Jan 1 1992
Externally publishedYes

ASJC Scopus subject areas

  • Artificial Intelligence

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