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Classification system and method using combined information testing

  • US 5,999,893 A
  • Filed: 05/02/1997
  • Issued: 12/07/1999
  • Est. Priority Date: 05/02/1997
  • Status: Expired due to Fees
First Claim
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1. An information classification system combining training data and test data to classify a source which comprises:

  • a vector processor formulating a plurality of feature vectors depicting characteristics of said test data and for receiving said plurality of feature vectors wherein each of said plurality of feature vectors is quantized to one of M symbols and for generating a quantized test vector y having as components the number of occurrences of each of the M symbols in said plurality of quantized feature vectors received;

    means for storing quantized training data, said quantized training data having one quantized training vector xc for each class of a plurality of output classes; and

    a classification processor, responsive to said quantized test vector and said quantized training data, for estimating sysmbol probabilities for each output class and for classifying the quantized test vector y unto one of said plurity output classes, wherein said classification processor estimates the symbol probabilities for each output class and classifies the quantized test vector using a combined Bayes test given by ##EQU15## wherein Xk,i is the number of occurrences of the ith symbol in a quantized training vector for class k;

    xl,i is the number of occurrences of the ith symbol in a quantized training vector for class l;

    yi is the number of occurrences of the ith symbol in the quantized test vector;

    ##EQU16## is the total number of occurrences of the M symbols in the training data for class k;

    ##EQU17## is the total number of occurrences of the M symbols in the training data for class l; and

    ##EQU18## is the total number of occurrences of the M symbols in the quantized feature vectors.

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