Fujitsu Corporation

Kyushu University

Kotaro Ohori

Keisuke Goto

Hiroaki Iwashita

Shohei Yamane

Hiroaki Yamada

Tatsuya Asai

Yuri Nakao

Shingo Takahashi

Takuya Takagi

Hiroaki Yoshida

Takashi Kato

Hirokazu Anai

Takuya Ohwa

Naoyuki Kamiyama

Akifumi Kira

Yoshinobu Shiota

With respect to training data records in which combinations of data item values of data items are individually associated with label information, the data item values are converted based on a criterion per data item into discretized data values. Training processing for training a model that receives the discretized data values as input and performs determination about the label information is performed by using training data records obtained by the conversion. From an execution result of the training processing, feature data records, each of which differently indicates a combination of two or more data items for the determination among the data items, and index values, which indicate importance levels of the feature data records respectively, are acquired. The criterion for the discretization of the data item values is changed based on at least one of the feature data records having been selected based on the index values.

Method and apparatus for training machine learning model using discretization

A non-transitory computer-readable recording medium stores therein a prediction program that causes a computer to execute a process including receiving input data to be predicted, generating a tree structure data based on a plurality of pieces of rule information each indicated by an association of a combination of attribute values of a plurality of attributes with a label according to a predetermined order condition for the plurality of attributes, the tree structure data being obtained by aggregating the plurality of pieces of rule information, the tree structure data including an attribute value as a branch, and determining a degree of contribution to make a determination result on a predetermined value of a predetermined attribute reach a predetermined label based on a likelihood of obtaining a value of the predetermined label as the determination result, when the attribute value of the predetermined attribute is determined to be the predetermined value.

Prediction method, prediction apparatus, and computer-readable recording medium

A non-transitory computer-readable recording medium stores therein a prediction program that causes a computer to execute a process including obtaining a machine learning model trained by using training data, the machine learning model predicting presence or absence of purchase actions or predetermined actions of users corresponding to feature information on the users, the training data including feature information on users and information indicating the presence or the absence of the purchase actions of commercial products or the predetermined actions, receiving input of a budget amount in an entry field displayed on a display in association with a user group including users including a common feature in the feature information, and displaying, in association with the budget amount on the display, feature information on the users included in the user group corresponding to the entry field.

Prediction method, prediction device, and computer-readable recording medium

A non-transitory computer-readable recording medium stores therein an extraction program that causes a computer to execute a process including: generating a plurality of combinations of conditions relating to a plurality of item values included in data; calculating an index value that indicates a degree of cooccurrence between a specified response variable and each of the plurality of combinations, by using a machine learning model that estimates a response variable from the plurality of item values, the machine learning model having been trained by using the data; and extracting a specific combination from among the plurality of combinations based on any one of the condition and the index value.

Extraction method, extraction device, and computer-readable recording medium

A recording medium records a simulation program for causing a computer to execute a process including: arranging an agent, which has cognitive information and behaves in a virtual space based on the cognitive information, in the virtual space in which a plurality of guidance displays is set; when, with respect to each of the plurality of guidance displays, the agent enters within a first range of the respective guidance displays, and first guidance information corresponding to the first range is related with a destination of the agent, changing cognitive information up to the destination of the agent; and when, with respect to each of the plurality of guidance displays, the agent enters within a second range different from the first range, and second guidance information corresponding to the second range is related with the destination of the agent, changing cognitive information not directly related with the destination of the agent.

Recording medium recording simulation program, simulation method, and information processing device for people flow in sign system

A non-transitory computer-readable recording medium stores therein a predicting program that causes a computer to execute: receiving input data to be subjected to prediction; and generating, from training data sets each having explanatory variables and an objective variable, a prediction result, by using a hypothesis set and respective weights of hypotheses included in the hypothesis set, the hypotheses each being formed of a combination of the explanatory variables, classifying any of the training data sets and satisfying a specific condition, the weights being learnt based on whether each of the hypotheses holds true for each of the training data sets. The generating includes determining a value of a variable included in a pseudo-Boolean function such that a probability satisfies a predetermined standard, the probability being a probability that the prediction result satisfies the specific condition, the pseudo-Boolean function including variables corresponding to the explanatory variables.

Predicting method, predicting apparatus, and computer-readable recording medium

An item presentation method implemented by a computer, the item presentation method includes: executing a selection processing that includes selecting a plurality of candidate items that are presentation candidates from a plurality of items, based on a preference of a user; executing a calculation processing that includes calculating the number of new items newly presented among the plurality of candidate items by referring to an item presentation history to the user; executing a decision processing when the number of new items is a predetermined number or less, the decision processing including deciding, as a recommendation item, an item located away from the preference in an item space about the preference by a distance less than a threshold value; and executing a presentation processing that includes selecting a presentation item presented to the user from the plurality of candidate items and the recommendation item, and presenting the selected presentation item.

Item presentation method, item presentation program, and item presentation apparatus

A simulation method is performed by a computer by using an agent disposed in a virtual space. The method includes: controlling the agent so that the agent behaves in the virtual space under influence of each of a plurality of signs disposed in the virtual space; and determining the influence of each of the plurality of signs on the agent in accordance with an attribute relating to a display mode of the sign and an attribute of the agent.

Simulation program, simulation method, and simulation apparatus

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.

Computer-readable recording medium, estimation method and estimation device

A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process including: inputting, to each of a plurality of agents, a plurality of purchasing behavioral objectives, a first purchasing behavioral objective, and facilities information indicating a plurality of facilities corresponding to the plurality of purchasing behavioral objectives; and causing each of the plurality of agents to determine, based on a condition that has been set in accordance with a facility search behavior with respect to a facility that is selected from the plurality of facilities in response to the purchasing behavioral objective of each of the plurality of agents at a time of execution of simulation, a second purchasing behavioral objective of each of the plurality of agents from among the plurality of purchasing behavioral objectives.

Computer-readable recording medium, simulation method, and simulation apparatus

A non-transitory computer-readable recording medium stores therein a matching program that causes a computer to execute a process including: determining subgame perfect equilibrium of a game in extensive form corresponding to the matching, the game in extensive form having a structure in which a plurality of independent players have respective gains corresponding to respective preferences of the respective applicants, and have the ranks of priority associated with order of play, and each of the players recognizes the gains of all the players; performing matching based on results of the subgame perfect equilibrium; setting the gain of a player corresponding to a coupling-requesting applicant having a coupling request requesting matching as a group with another specific applicant, in accordance with whether the coupling request is satisfied; and determining whether to prune options in a move of the player corresponding to the coupling-requesting applicant.

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Sumida, Japan

Kotaro Ohori