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PAC analyses of a ‘similarity learning’ IBL algorithm

  • A. D. Griffiths
  • D. G. Bridge
Scientific Papers Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1266)

Abstract

VS-CBR [14] is a simple instance-based learning algorithm that adjusts a weighted similarity measure as well as collecting cases. This paper presents a ‘PAC’ analysis of VS-CBR, motivated by the PAC learning framework, which demonstrates two main ideas relevant to the study of instance-based learners. Firstly, the hypothesis spaces of a learner on different target concepts can be compared to predict the difficulty of the target concepts for the learner. Secondly, it is helpful to consider the ‘constituent parts’ of an instance-based learner: to explore separately how many examples are needed to infer a good similarity measure and how many examples are needed for the case base. Applying these approaches, we show that VS-CBR learns quickly if most of the variables in the representation are irrelevant to the target concept and more slowly if there are more relevant variables. The paper relates this overall behaviour to the behaviour of the constituent parts of VS-CBR.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • A. D. Griffiths
    • 1
  • D. G. Bridge
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

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