Journal
ARTIFICIAL INTELLIGENCE REVIEW
Volume 18, Issue 2, Pages 77-95Publisher
SPRINGER
DOI: 10.1023/A:1019956318069
Keywords
classification; inductive learning; meta-knowledge
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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.
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