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Approaches to machine learning

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  • Pat Langley
  • Jaime G. Carbonell

Abstract

The field of machine learning strives to develop methods and techniques to automate the acquisition of new information, new skills, and new ways of organizing existing information. This article reviews the major approaches to machine learning in symbolic domains, illustrated with occasional paradigmatic examples.

Suggested Citation

  • Pat Langley & Jaime G. Carbonell, 1984. "Approaches to machine learning," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 35(5), pages 306-316, September.
    • Handle: RePEc:bla:jamest:v:35:y:1984:i:5:p:306-316
    DOI: 10.1002/asi.4630350509
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    Cited by:

    1. Hui Zheng & Peng LI & Jing HE, 2022. "A Novel Association Rule Mining Method for Streaming Temporal Data," Annals of Data Science, Springer, vol. 9(4), pages 863-883, August.

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