Reduction Techniques for Exemplar-Based Learning Algorithms

@inproceedings{Wilson2000ReductionTF,
  title={Reduction Techniques for Exemplar-Based Learning Algorithms},
  author={D. Randall Wilson and Tony R. Martinez},
  year={2000}
}
Exemplar-based learning algorithms are often faced with the problem of deciding which instances or other exemplars to store for use during generalization. Storing too many exemplars can result in large memory requirements and slow execution speed, and can cause an oversensitivity to noise. This paper has two main purposes. First, it provides a survey of… CONTINUE READING

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