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Spam filtering using feature relevance assignment in neural networks

  • US 8,131,655 B1
  • Filed: 05/30/2008
  • Issued: 03/06/2012
  • Est. Priority Date: 05/30/2008
  • Status: Active Grant
First Claim
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1. A spam filtering method comprising employing a computer system to perform the steps of:

  • computing a set of pattern relevancies for a set of feature patterns, wherein at least one pattern relevance of the set of pattern relevancies is computed according to a set of feature relevance weights determined through a process external to neuronal training; and

    classifying a target message as spam or ham according to a result of a processing of the target message by a neural network filter according to the set of pattern relevancies by;

    assigning a pattern relevance of the set of pattern relevancies to each neuron of a subset of neurons of the neural network filter;

    computing a target input vector characterizing the presence of a set of spam/ham identifying message features within the target message;

    selecting an active neuron of the subset of neurons according to a scalar product between the target input vector and a first set of neuronal weights of the neural network filter;

    computing a recognition score according to a scalar product between the target input vector and a second set of neuronal weights of the neural network filter, and according to the pattern relevance corresponding to the active neuron; and

    comparing the recognition score to a predefined vigilance threshold.

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