Provided is an apparatus, method, and computer-readable storage medium for acquiring learning data that includes an evaluation of evaluation targets; generating a constraint condition to be satisfied by a value of an evaluation function that includes a weighting for each of a plurality of evaluation criteria of the evaluation target and an unknown term for each evaluation target corresponding to an unknown evaluation criterion that is not among the plurality of evaluation criteria, based on the learning data; determining a value of the unknown term and the weighting for each evaluation criterion in the evaluation function in a manner that satisfies the constraint condition; extracting a set of evaluation targets for which an evaluation of each evaluation criterion is opposite of an evaluation based on the unknown term, from among the plurality of evaluation targets; and outputting the extracted set of evaluation targets.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus comprising: a processor; and one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to: acquire learning data that includes an evaluation of evaluation targets; generate a constraint condition to be satisfied by a value of an evaluation function that includes a weighting for each of a plurality of evaluation criteria of the evaluation target and an unknown term for each evaluation target corresponding to an unknown evaluation criterion that is not among the plurality of evaluation criteria, based on the learning data; determine a value of the unknown term and the weighting for each evaluation criterion in the evaluation function in a manner that satisfies the constraint condition; extract a set of evaluation targets for which an evaluation of each evaluation criterion is opposite of an evaluation based on the unknown term, from among the plurality of evaluation targets; and output the extracted set of evaluation targets. 2. The apparatus according to claim 1, wherein the extracting includes extracting, from the plurality of evaluation targets, a set of a first evaluation target and a second evaluation target, and wherein the second evaluation target has: lower evaluation values, for all of the evaluation criteria, than the first evaluation target; and a higher evaluation value, for an evaluation based on the unknown term, than the first evaluation target. 3. The apparatus according to claim 2, wherein: the extracting includes extracting. from the plurality of evaluation targets, a set of a plurality of first evaluation targets and a plurality of second evaluation targets by using an objective function including a term corresponding to a difference in the unknown term between the plurality of first evaluation targets and the plurality of second evaluation targets. 4. The apparatus according to claim 3, wherein: the extracting includes using the objective function, further including a term corresponding to the number of the first evaluation targets and the second evaluation targets. 5. The apparatus according to claim 4, wherein: the extracting includes adjusting the number of the first evaluation targets and the second evaluation targets that are extracted, by adjusting a coefficient of a term corresponding to the number of the first evaluation targets and the second evaluation targets. 6. The apparatus according to claim 1, wherein: the outputting includes outputting the extracted set of evaluation targets to a user; and the processor is further caused to receive, from the user, designation of an evaluation criterion to be added to the plurality of evaluation criteria. 7. The apparatus according to claim 6, wherein: the generating includes further generating, based on the learning data, the constraint condition to be satisfied by the value of the evaluation function including a weighting for each evaluation criterion added according to the designation made by the user and an unknown term for each evaluation target corresponding to a new unknown evaluation criterion that is not among the plurality of evaluation criteria. 8. The apparatus according to claim 1, wherein: the acquiring includes acquiring the learning data that includes, as the evaluation, a qualitative evaluation that is a comparison result obtained by qualitatively comparing two or more evaluation targets. 9. The apparatus according to claim 8, wherein: the acquiring includes acquiring the learning data that further includes, as the evaluation, a comparison result obtained by qualitatively comparing the evaluation targets to a predetermined evaluation standard. 10. The apparatus according to claim 8, wherein: the generating includes generating, as the constraint condition, an inequation that includes a difference in evaluation values of the evaluation function for two or more of the evaluation targets being compared and an evaluation threshold value that serves as a standard for the qualitative evaluation; and the determining includes determining the evaluation threshold value in a manner to satisfy the constraint condition. 11. The apparatus according to claim 10, wherein: the acquiring includes acquiring the learning data including a plurality of the qualitative evaluations made by a plurality of evaluating subjects; and the generating includes generating, as the constraint condition, an inequation that includes the evaluation threshold value for each evaluating subject. 12. The apparatus according to claim 11, wherein: the processor is further caused to judge whether the difference in the evaluation values between the two or more evaluation targets according to the evaluation function based on the weighting determined during the determining is within a predetermined reference range relative to the evaluation threshold value; and the acquiring includes acquiring an additional qualitative evaluation and adding the additional qualitative evaluation to the learning data, in response to the difference in evaluation values being judged to be within the reference range relative to the evaluation threshold value. 13. The apparatus according to claim 12, wherein: the processor is further caused to present the evaluating subject with the two or more evaluation targets for which the difference in the evaluation values therebetween is within the reference range; and the acquiring includes acquiring the qualitative evaluation made by the evaluating subject for the presented two or more evaluation targets, and adding the acquired qualitative evaluation to the learning data. 14. The apparatus according to claim 1, wherein: the generating includes generating the constraint condition based on the evaluation function that includes the unknown term and a term that is based on a total weighting of a plurality of basis functions into which are input a characteristic value for each evaluation criterion of the evaluation targets; and the determining includes determining the unknown term and the weighting of each of the plurality of basis functions such that the constraint condition is satisfied. 15. The apparatus according to claim 14, wherein: the generating includes generating the constraint condition including a variable that indicates whether each of the plurality of basis functions is included; and the determining includes optimizing the weighting by using an objective function including the total number of basis functions included in the evaluation function. 16. The apparatus according to claim 15, wherein: the generating includes generating the objective function including an error variable; and the determining includes optimizing the weighting by using the objective function including the error variable. 17. A computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to: acquire learning data that includes an evaluation of evaluation targets; generate a constraint condition to be satisfied by a value of an evaluation function that includes a weighting for each of a plurality of evaluation criteria of the evaluation target and an unknown term for each evaluation target corresponding to an unknown evaluation criterion that is not among the plurality of evaluation criteria, based on the learning data; determine a value of the unknown term and the weighting for each evaluation criterion in the evaluation function in a manner that satisfies the constraint condition; extract a set of evaluation targets for which an evaluation of each evaluation criterion is opposite of an evaluation based on the unknown term, from among the plurality of evaluation targets; and output the extracted set of evaluation targets. 18. A method comprising: acquiring learning data that includes an evaluation of evaluation targets; generating a constraint condition to be satisfied by a value of an evaluation function that includes a weighting for each of a plurality of evaluation criteria of the evaluation target and an unknown term for each evaluation target corresponding to an unknown evaluation criterion that is not among the plurality of evaluation criteria, based on the learning data; determining a value of the unknown term and the weighting for each evaluation criterion in the evaluation function in a manner that satisfies the constraint condition; extracting a set of evaluation targets for which an evaluation of each evaluation criterion is opposite of an evaluation based on the unknown term, from among the plurality of evaluation targets; and outputting the extracted set of evaluation targets.