Rule Representations in a Connectionist Chunker

Abstract

Two connectionist architectures are presented for chunking of symbolic rewrite rules. One uses backpropagation learning, the other competitive learning. Although they were developed for chunking the same sorts of rules, the two differ in their representational abilities and learning behaviors. Keywords: Rule learning; Connectionist architectures; Sequence manipulation; Computer programs.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1990
Accession Number
ADA225535

Entities

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  • David S. Touretzky
  • Gillette Elvgren Iii

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  • Carnegie Mellon University

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