Fujitsu Corporation

Takuya Ohwa

Hiroaki Yoshida

Hidetoshi Matsuoka

Kotaro Ohori

Yuri Nakao

A method includes: partitioning learning data containing objective variables and explanatory variables into a plurality of subsets of data; executing regularization processing on first data in each of the partitioned subsets, and extracting a first element equal to zero; extracting, as a candidate, each model where an error ratio between first multiple regression and second multiple regression is equal to or more than a predetermined value, the first multiple regression being a result of multiple regression on second data which is test data in each of the partitioned subsets and is for use to calculate the error ratio of the learning data, the second multiple regression being a result of multiple regression on third data obtained by excluding the first element from the second data; and outputting a model where zero is substituted for an element that takes zero a predetermined or larger number of times in the candidate.

Optimization apparatus, non-transitory computer-readable storage medium for storing optimization program, and optimization method

A search device, for each of a plurality of inquiries including a plurality of options, based on a log of answers of a respondent in the past, specifies a matching option corresponding to a trend of answers of the respondent from the plurality of options to be represented to the respondent. The search device, when an answer to the plurality of options is included in the matching option, determines that it is a preference-reached mode that is a mode where the estimating a preference of the user converges. The search device transmits, to the respondent, an inquiry including options, contents of the options included in the inquiry being changed depend on whether it is determined to be a preference-reached mode in the determining.

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Shinagawa, Japan

Takuya Ohwa