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  1. A logical calculus of the ideas immanent in nervous activity.Warren S. McCulloch & Walter Pitts - 1943 - The Bulletin of Mathematical Biophysics 5 (4):115-133.
    Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic. It is found that the behavior of every net can be described in these terms, with the addition of more complicated logical means for nets containing circles; and that for any logical expression satisfying certain conditions, one can find a net behaving in the fashion it describes. It is shown that many particular choices among possible neurophysiological assumptions (...)
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  • Theory refinement combining analytical and empirical methods.Dirk Ourston & Raymond J. Mooney - 1994 - Artificial Intelligence 66 (2):273-309.
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  • Transcending inductive category formation in learning.Roger C. Schank, Gregg C. Collins & Lawrence E. Hunter - 1986 - Behavioral and Brain Sciences 9 (4):639-651.
    The inductive category formation framework, an influential set of theories of learning in psychology and artificial intelligence, is deeply flawed. In this framework a set of necessary and sufficient features is taken to define a category. Such definitions are not functionally justified, are not used by people, and are not inducible by a learning system. Inductive theories depend on having access to all and only relevant features, which is not only impossible but begs a key question in learning. The crucial (...)
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  • Integrated Learning: Controlling Explanation.Michael Lebowitz - 1986 - Cognitive Science 10 (2):219-240.
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  • Models of incremental concept formation.John H. Gennari, Pat Langley & Doug Fisher - 1989 - Artificial Intelligence 40 (1-3):11-61.
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  • The role of theories in conceptual coherence.Gregory L. Murphy & Douglas L. Medin - 1985 - Psychological Review 92 (3):289-316.
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  • Connectionist learning procedures.Geoffrey E. Hinton - 1989 - Artificial Intelligence 40 (1-3):185-234.
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