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Concept Learning and the General-to-Specific Ordering of Hypotheses in Machine Learning

  • Unique Paper ID: 156543
  • Volume: 9
  • Issue: 4
  • PageNo: 134-137
  • Abstract:
  • The problem of inducing general functions from specific training examples is central to learning. This paper considers concept learning: acquiring the definition of a general category given a sample of positive and negative training examples of the category. Concept learning can be formulated as a problem of searching through a predefined space of potential hypotheses for the hypothesis that best fits the training examples. In many cases this search can be efficiently organized by taking advantage of a naturally occurring structure over the hypothesis space-a generalto-specific ordering of hypotheses. This chapter presents several learning algorithms and considers situations under which they converge to the correct hypothesis. We also examine the nature of inductive learning and the justification by which any program may successfully generalize beyond the observed training data
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 9
  • Issue: 4
  • PageNo: 134-137

Concept Learning and the General-to-Specific Ordering of Hypotheses in Machine Learning

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Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

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