Automated Concept Acquisition in Noisy Environments

Francesco Bergadano, Attilio Giordana, Lorenza Saitta

Risultato della ricerca: Contributo su rivistaArticolo in rivistapeer review

Abstract

This paper presents a system which performs automated concept acquisition from examples and has been especially designed to work in noisy environments. The learning methodology is aimed at the target problem of finding discriminant descriptions of a given set of concepts and uses both examples and counterexamples. The learned knowledge is expressed in the form of production rules, organized into separate clusters, linked together in a graph structure; the condition part of the rules, corresponding to descriptions of relevant aspects of the concepts, is expressed by means of a language based on first order logic, enriched with constructs suitable for handling uncertainty and vagueness and increasing readability for a human user. A continuous-valued semantics is associated to this language and each rule is affected by a certainty factor. Learning is considered as a cyclic process of knowledge extraction, validation, and refinement; the control of the cycle is left to the teacher. Knowledge extraction is guided by a topdown control strategy, through a process of specialization. The system also utilizes a technique of problem reduction to contain the computational complexity. Moreover, the search strategy is strongly focalized by means of task-oriented but domain-independent heuristics, in an attempt to emulate the learning mechanism of a human being, who has to find discrimination rules from a set of examples. Several criteria are proposed for evaluating the acquired knowledge; these criteria are used to guide the process of knowledge refinement. The methodology has been tested on a problem in the field of speech recognition and the experimental results obtained are reported and discussed.

Lingua originaleInglese
pagine (da-a)555-578
Numero di pagine24
RivistaIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume10
Numero di pubblicazione4
DOI
Stato di pubblicazionePubblicato - lug 1988
Pubblicato esternamente

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