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公开(公告)号：US20240119296A1
公开(公告)日：2024-04-11
申请号：US18276290
申请日：2021-06-07
Applicant: NEC Corporation
Inventor： Akira TANIMOTO , Tomoya SAKAI , Takashi TAKENOUCHI , Hisashi KASHIMA
IPC: G06N3/09
CPC classification number: G06N3/09
Abstract: A learning device calculates an estimation target item reference value according to a fixed value of each estimation target object. The learning device acquires learning data that includes the fixed value of each estimation target object, a variable item value, and an estimation target item value according to the fixed value and the variable item value. The learning device trains, using the learning data and an evaluation function, a model that outputs an estimated value of the estimation target item value in response to input of the fixed value of each estimation target object and the variable item value.
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公开(公告)号：US20220245518A1
公开(公告)日：2022-08-04
申请号：US17610488
申请日：2019-05-22
Applicant: NEC Corporation
Inventor： Masato ISHII , Takashi TAKENOUCHI , Masashi SUGIYAMA
Abstract: A data transformation apparatus (1) includes: data transformation means (11) for performing data transformation on each of a plurality of data sets so that data distributions of the plurality of data sets are brought close to each other; first calculation means (12) for calculating a class classification loss from a result of class classification performed by class classification means on at least some of a plurality of first transformed data sets obtained after the data transformation; second calculation means (13) for calculating an upper bound and a lower bound of a domain classification loss from a result of domain classification performed by domain classification means on each of the plurality of first transformed data sets; and first learning means (14) for performing first learning by updating a parameter of the domain classification means so that the upper bound is reduced and updating a parameter of the data transformation means so that the class classification loss is reduced and the lower bound is increased.
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公开(公告)号：US20220121990A1
公开(公告)日：2022-04-21
申请号：US17422678
申请日：2019-01-22
Applicant: NEC Corporation
Inventor： Masato ISHII , Takashi TAKENOUCHI , Masashi SUGIYAMA
Abstract: A data conversion learning apparatus includes a data conversion unit that performs data conversion of source data and target data, a first deduction unit that deduces data of a non-appearing class on the basis of a domain certainty factor acquired by a domain identification using converted data, a second deduction unit that deduces data of a non-appearing class on the basis of a class certainty factor acquired by a class identification using converted data, a class identification learning unit that performs machine learning for class identification using the data of the non-appearing class deduced by the first deduction unit and the source data and the target data which are inputs, and a domain identification learning unit that performs machine learning for domain identification using the data of the non-appearing class deduced by the second deduction unit and the source data and the target data which are inputs.
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公开(公告)号：US20210019636A1
公开(公告)日：2021-01-21
申请号：US17043309
申请日：2018-05-11
Applicant: NEC Corporation
Inventor： Masato ISHII , Takashi TAKENOUCHI , Masashi SUGIYAMA
Abstract: This prediction model preparation device is provided with: a calculation means which calculates, from a datum in which a sample and a label are associated with each other, an importance level according to the difference between a first possibility that an event influencing the sample occurs in a source domain and a second possibility that the event occurs in a target domain; and a preparation means which constructs prepares a prediction model relating to the target domain by calculating association between the sample and the label included in the datum to which the importance level is added.
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