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Adaptive statistical classifier which provides reliable estimates or output classes having low probabilities

  • US 5,805,731 A
  • Filed: 08/08/1995
  • Issued: 09/08/1998
  • Est. Priority Date: 08/08/1995
  • Status: Expired due to Term
First Claim
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1. A method for training a statistical classifier, comprising the following steps:

  • selecting a training sample from a collection of training samples, each such training sample being associated with a label class from a predetermined set of distinct classes;

    providing data pertaining to said training sample as an input signal to the classifier;

    processing said data within the classifier in accordance with weight values to produce a plurality of output signals which respectively correspond to different classes in said predetermined set of distinct classes,providing a plurality of target signals which respectively correspond to different classes in said predetermined set of distinct classes, wherein the target signal corresponding to said label class is assigned a first predetermined signal value, and the others of said target signals are assigned a second predetermined signal value;

    determining error signals corresponding to each of said distinct classes, based on differences between said output signals and said target signals;

    multiplying said error signal which corresponds to said label class by a factor β

    , where β

    >

    1; and

    adjusting said weight values in accordance with said modified error signals.

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