A fuzzy-set-theoretic feature model and its application to asymmetric similarity data analysis

研究成果: Article査読

6 被引用数 (Scopus)

抄録

Feature representation models are too restricted in that they consider features as dichotomous variables. In an attempt to construct a more general feature model, it is argued that fuzzy set theory (Zadeh, 1965) gives a natural and promising solution to the problem. Using fuzzy set theory in place of ordinary set theory, a fuzzy feature matching model, which is a generalization of Tversky's contrast model (1977) of similarity, is proposed and is applied to the analysis of an asymmetric similarity matrix, which was obtained by asking 42 undergraduates to judge pairwise similarity among eight countries.

本文言語English
ページ(範囲)95-104
ページ数10
ジャーナルJapanese Psychological Research
30
3
DOI
出版ステータスPublished - 1988

ASJC Scopus subject areas

  • 心理学(全般)

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