Abstract
Most of machine learning is concerned with learning a single concept from a sequence of examples. In repeat learning the teacher chooses a series of related concepts randomly and independently from a distribution D. A finite sequence of examples is provided for each concept in the series. The learner does not initially know D, but progressively updates a posterior estimation of D as the series progresses. This paper considers predicate invention within Inductive Logic Programming as a mechanism for updating the learner's estimation of D. A new predicate invention mechanism implemented in Progol4.4 is used in repeat learning experiments within a chess domain. The results indicate that significant performance increases can be achieved. The paper develops a Bayesian framework and demonstrates initial theoretical results for repeat learning.
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© 1998 Springer-Verlag Berlin Heidelberg
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Khan, K., Muggleton, S., Parson, R. (1998). Repeat learning using predicate invention. In: Page, D. (eds) Inductive Logic Programming. ILP 1998. Lecture Notes in Computer Science, vol 1446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0027320
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DOI: https://doi.org/10.1007/BFb0027320
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