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Verfasser Titel Jahr
zeige Details Belew, Richard K., Forrest, Stephanie Learning and programming in classifier systems
in: Machine learning , ISSN 0885-6125, Vol. 3 (2/3. 1988), p. 193-223
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1988
Abstract Both symbolic and subsymbolic models contribute important insights to our understanding of intelligent systems. Classifier systems are low-level learning systems that are also capable of supporting representations at the symbolic level. In this paper, we explore in detail the issues surrounding the integration of programmed and learned knowledge in classifier-system representations, including comprehensibility, ease of expression, explanation, predictability, robustness, redundancy, stability, and the use of analogical representations. We also examine how these issues speak to the debate between symbolic and subsymbolic paradigms. We discuss several dimensions for examining the tradeoffs between programmed and learned representations, and we propose an optimization model for constructing hybrid systems that combine positive aspects of each paradigm.
Copyright: Copyright 1988 Kluwer Academic Publishers
beteiligte Personen: Belew, Richard K. , Forrest, Stephanie
Format: Elektronisch
Erschienen: 1988.
Serie: Springer Online Journal Archives 1860-2000 [Dig. Serial]
Schlagwörter:
URL: http://dx.doi.org/10.1007/BF00113897

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zeige Details Belew, Richard K., Forrest, Stephanie Learning and Programming in Classifier Systems
in: Machine learning , ISSN 0885-6125, Vol. 3 (2/3. 1988), p. 193-223
Zugang: zum Volltext
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