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Reducing the size of a training set for classification

  • US 20070260566A1
  • Filed: 04/11/2006
  • Published: 11/08/2007
  • Est. Priority Date: 04/11/2006
  • Status: Active Grant
First Claim
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1. A method for reducing the size of a design data set, comprising:

  • computing a decision boundary which separates a first group of data patterns in a training data set from a second group of data patterns in the training data set;

    for each data pattern in the training data set, determining if removing the data pattern from the training data set substantially affects the resulting decision boundary; and

    if so, marking the data pattern as a key pattern; and

    removing all data patterns that are not marked as key patterns to produce a reduced training data set which represents the decision boundary.

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