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Probabilistic model generation method, apparatus, and program

  • US 20070038558A1
  • Filed: 08/01/2006
  • Published: 02/15/2007
  • Est. Priority Date: 08/12/2005
  • Status: Active Grant
First Claim
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1. A probabilistic model generation method for generating a probabilistic model calculating a probability that a predetermined event occurs or does not occur, by using learning data as a set of samples each of which includes a plurality of explanatory variables belonging to respectively different attributes and a target variable representing whether the predetermined event occurs or not, comprising:

  • optimizing a first objective function defined by using the explanatory variable belonging the attribute in each sample, the target variable in each sample, and a first conversion parameter to find a value of the first conversion parameter as for each of the attributes;

    generating by using the first conversion parameter corresponding to the attribute a conversion function for converting an explanatory variable belonging to the attribute to an intermediate variable with certain range of value as for each of the attributes;

    optimizing a second objective function defined by using a plurality of intermediate variables corresponding to the plurality of explanatory variables in each sample, the target variable in each sample, and a second conversion parameter to find a value of the second conversion parameter; and

    generating by using the second conversion parameter a probabilistic model for calculating from a plurality of intermediate variables a probability that the predetermined event occurs or does not occur.

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