| 摘要 |
<P>PROBLEM TO BE SOLVED: To learn an analysis model enabling acquisition of high degree of classification accuracy while preventing a calculation cost from increasing. <P>SOLUTION: A baseline analysis part 2 analyzes prediction values of analysis results, with respect to an analysis object, a basic feature amount and each of multiple training samples including correct answers. A rule candidate generation part 4 generates conversion rule candidates from the training samples with analysis errors in accordance with a rule template 5. A rule selection part 6 selects a conversion rule candidate with the maximum net increase number of correct answers when applying each of the conversion rule candidates. A rule application part 8 applies the selected conversion rule to all the training samples, and repeats generation and application of the rule until the analysis errors vanish. An index generation part 10 stores a history of the rule applied to each of the training samples and an index of the basic feature amount. A training vector generation part 12 generates a training vector on the basis of the index. A learning part 14 learns an analysis model on the basis of the training vector. <P>COPYRIGHT: (C)2013,JPO&INPIT |
