Fujitsu Corporation

Shunichi Watanabe

Izumi Nitta

Daisuke Fukuda

Tetsuyoshi Shiota

Satoko Iwakura

Masaru Todoriki

A machine learning method includes acquiring data including attendance records of employees and information indicating which employee has taken a leave of absence from work, in response to determining that a first employee of the employees has not taken a leave of absence in accordance with the data, generating a first tensor on a basis of an attendance record of the first employee and parameters associated with elements included in the attendance record, in response to determining that a second employee of the employees has taken a leave of absence in accordance with the data, modifying the parameters, and generating a second tensor on a basis of an attendance record of the second employee and the modified parameters, and generating a model by machine learning based on the first tensor and the second tensor.

Machine learning method

A learning device receives, for each target, learning data that represents the source of generation of a tensor including a plurality of elements which multi-dimensionally represent the features of the target over a period of time set in advance. When the target satisfies a condition set in advance, the learning device identifies the period of time corresponding to the condition in the learning data. Subsequently, the learning device generates a weighted tensor corresponding to the learning data that is at least either before or after the concerned period of time.

Computer-readable recording medium and learning data generation method

A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process comprising: acquiring a first feature from a machine learning model that estimates a first result of a target after a first period in response to an input of a first chronological state of the target in the first period, the first feature being a feature of the first chronological state; acquiring a second feature by inputting a second chronological state to the machine learning model, the second feature being a feature of the second chronological state in a second period including a period after the first result is determined; and generating, based on the first feature and the second feature, training data that takes the second chronological state as an explanatory variable and takes a second result as an objective variable, the second result being obtained by changing the determined first result.

Growing community of inventors

Kawasaki, Japan

Shunichi Watanabe