A perspective view and survey of meta-learning

  • Authors:
  • Ricardo Vilalta;Youssef Drissi

  • Affiliations:
  • IBM T.J. Watson Research Center, 30 Saw Mill River Rd, Hawthorne NY;IBM T.J. Watson Research Center, 30 Saw Mill River Rd, Hawthorne NY

  • Venue:
  • Artificial Intelligence Review
  • Year:
  • 2002

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Abstract

Different researchers hold different views of what the term meta-learning exactly means. The first part of this paper provides our own perspective view in which the goal is to build self-adaptive learners (i.e. learning algorithms that improve their bias dynamically through experience by accumulating meta-knowledge). The second part provides a survey of meta-learning as reported by the machine-learning literature. We find that, despite different views and research lines, a question remains constant: how can we exploit knowledge about learning (i.e. meta-knowledge) to improve the performance of learning algorithms? Clearly the answer to this question is key to the advancement of the field and continues being the subject of intensive research.