ON Case-Based Representability and Learnability of Languages ?

  • Within the present paper we investigate case-based representability as well as case-based learnability of indexed families of uniformly recursive languages. Since we are mainly interested in case-based learning with respect to an arbitrary fixed similarity measure, case-based learnability of an indexed family requires its representability, first. We show that every indexed family is case- based representable by positive and negative cases. If only positive cases are allowed the class of representable families is comparatively small. Furthermore, we present results that provide some bounds concerning the necessary size of case bases. We study, in detail, how the choice of a case selection strategy influences the learning capabilities of a case-based learner. We define different case selection strategies and compare their learning power to one another. Furthermore, we elaborate the relations to Gold-style language learning from positive and both positive and negative examples.

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Metadaten
Verfasserangaben:Christoph Globig, Steffen Lange
URN (Permalink):urn:nbn:de:hbz:386-kluedo-906
Dokumentart:Preprint
Sprache der Veröffentlichung:Englisch
Jahr der Fertigstellung:1994
Jahr der Veröffentlichung:1994
Veröffentlichende Institution:Technische Universität Kaiserslautern
Datum der Publikation (Server):03.04.2000
Freies Schlagwort / Tag:Case-Based Representability
Bemerkung:
This work has been supported by the Deutsche Forschungsgemeinschaft (DFG) within the project Ind-Cbl.
Fachbereiche / Organisatorische Einheiten:Fachbereich Informatik
DDC-Sachgruppen:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Lizenz (Deutsch):Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011

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