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Learning machine that considers global structure of data

  • US 20070168305A1
  • Filed: 10/18/2005
  • Published: 07/19/2007
  • Est. Priority Date: 10/18/2005
  • Status: Abandoned Application
First Claim
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1. A method for training a classifier comprising:

  • receiving input vectors from a dataset that have been clustered into one or more clusters;

    specifying generalized constraints which are dependent upon the clusters of input vectors in the dataset; and

    optimizing a separating hyperplane in a separating space subject to the generalized constraints, where the input vectors are mapped to the separating space using a kernel function and where the separating hyperplane is determined by a set of coefficients generated in accordance with the generalized constraints.

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