A Comparison of Rule and Exemplar-Based Learning Systems

Recently, there has been renewed interest in the use of exemplar-based schemes for concept representation and learning. In this paper, we compare systems learning concepts represented in this form with those which learn concepts represented by decision rules, such as the ID3 and AQU rule induction systems. We aim to clarify the distinction between the two representational schemes, and compare how systems based on the different schemes address the problem of leaming within finite resources. Our conclusions are that the schemes differ in two important ways: in the different ‘biases’ with which they select between alternative concepts during search and in the different computational approaches of generalising before or during a run-time task. We also show that in addressing the problem of finite resources important commonalities between implementations based on both representational schemes arise, and by highlighting them aim to encourage the transfer of techniques between the two paradigms.

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