| 摘要 |
When n-dimensional data which belongs to one class in an n-dimensional feature space defined by n types of variates and whose position is specified by the variates is input, the feature space is divided into mn divided areas by performing m-part division for each of the variates. In this division, a division number m is determined on the basis of a statistical significance level by regarding a degree of generation of a divided area containing one data as a degree following a probability distribution with respect to the division number m. A classification model is generated by classifying the generated divided areas depending on whether they contain n-dimensional data.
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