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Method of building predictive models on transactional data

  • US 6,873,979 B2
  • Filed: 02/28/2001
  • Issued: 03/29/2005
  • Est. Priority Date: 02/29/2000
  • Status: Active Grant
First Claim
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1. A method of building predictive models on transactional data, comprising:

  • providing an aggregation module for each transactional record source;

    initializing output values of each aggregation module;

    inputting a first transactional record from each transactional record source into said corresponding aggregation module;

    calculating a first iteration of said output values for each aggregation module as;


    ƒ

    ki(1)=F



    tpqwim),0),  

    where;

    φ

    is a neural network element function;

    F is a blending function that controls how fast a previous transactional record become obsolete; and

    Wim are weights of the neural network;

    inputting a next transactional record from each transactional record source into said corresponding aggregation module;

    updating said outputs values of each aggregation module as;


    ƒ

    ki(r+1)=F



    tpqwim),ƒ

    ki(r));

    repeating the two prior steps until all transactional records are processed; and

    obtaining scalar values ƒ

    ki as scalar inputs for traditional modeling.

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