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Scientific article
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English

Automatic' learning strategies and their application to electrophoresis analysis

Published inComputerized medical imaging and graphics, vol. 13, no. 5, p. 383-391
Publication date1989
Abstract

Automatic learning plays an important role in image analysis and pattern recognition. A taxonomy of automatic learning strategies is presented; this categorization is based on the amount of inferences the learning element must perform to bridge the gap between environmental and system knowledge representation level. Four main categories are identified and described: rote learning, learning by deduction, learning by induction, and learning by analogy. An application of learning by induction to medical image analysis is then exposed. It consists in the classification of two-dimensional gel electrophoretograms into meaningful distinct classes, as well in their conceptual description.

Keywords
  • Image analysis
  • Pattern recognition
  • Automatic learning
  • Artificial intelligence
  • Expert system
  • Conceptual clustering
  • Two-dimensional gel electrophoresis
Citation (ISO format)
ROCH, Christian Maurice et al. Automatic” learning strategies and their application to electrophoresis analysis. In: Computerized medical imaging and graphics, 1989, vol. 13, n° 5, p. 383–391. doi: 10.1016/0895-6111(89)90225-5
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ISSN of the journal0895-6111
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