K*: An Instance-based Learner Using and Entropic Distance Measure

@inproceedings{Cleary1995KAI,
  title={K*: An Instance-based Learner Using and Entropic Distance Measure},
  author={John G. Cleary and Leonard E. Trigg},
  booktitle={ICML},
  year={1995}
}
The use of entropy as a distance measure has several benefits. Amongst other things it provides a consistent approach to handling of symbolic attributes, real valued attributes and missing values. The approach of taking all possible transformation paths is discussed. We describe K*, an instance-based learner which uses such a measure, and results are presented which compare favourably with several machine learning algorithms. 
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