Using Background Knowledge to Build Multistrategy Learners

Authors:
Claude Sammut
Affiliations:
School of Computer Science and Engineering, University of New South Wales, Sydney, Australia 2052. E-mail: claude@cse.unsw.edu.au
Venue:
Machine Learning - Special issue on multistrategy learning
Year:
1997

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Abstract

This paper discusses the role that background knowledge can play inbuilding flexible multistrategy learning systems. We contend that avariety of learning strategies can be embodied in the backgroundknowledge provided to a general purpose learning algorithm. To beeffective, the general purpose algorithm must have a mechanism forlearning new concept descriptions that can refer to knowledgeprovided by the user or learned during some other task. The method ofknowledge representation is a central problem in designing such asystem since it should be possible to specify background knowledge insuch a way that the learner can apply its knowledge to newinformation.