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11.发明申请公开(公告)号：US20200042876A1
公开(公告)日：2020-02-06
申请号：US16653236
申请日：2019-10-15
Applicant: FUJITSU LIMITED
Inventor： TAKASHI KATOH , Kento UEMURA , Suguru YASUTOMI , Toshio Endoh , Koji MARUHASHI
Abstract: A non-transitory computer-readable recording medium records an estimation program causing a computer to execute processing which includes: calculating a reconfiguration error from an input result value and a reconfiguration value that is estimated by a first estimator, which estimates a parameter value from a result value learned on a basis of past data, and a second estimator, which estimates a result value from a parameter value, by using a specific result value or a neighborhood result value in a neighborhood of the specific result value; searching for a first result value that minimizes a sum of a substitute error that is calculated from the input result value and the specific result value and the reconfiguration error; and outputting a parameter value that is estimated from the first result value by using the first estimator.
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公开(公告)号：US20160154875A1
公开(公告)日：2016-06-02
申请号：US14936845
申请日：2015-11-10
Applicant: FUJITSU LIMITED
Inventor： Koji MARUHASHI , NOBUHIRO YUGAMI , Ryo Ochitani
IPC: G06F17/30
CPC classification number: G06F16/285
Abstract: A classification method executed by a computer for classifying a plurality of records into a plurality of groups, the classification method includes: acquiring the plurality of records, the plurality of records including a variable value respectively; tentatively classifying the plurality of records into the plurality of groups; calculating a commonality value indicating a degree of commonality of the variable value among the plurality of groups, based on the variable value included in each of the tentatively classified groups; classifying the plurality of records into the plurality of groups based on the commonality value; and outputting a result of the classifying.
Abstract translation: 一种由计算机执行的用于将多个记录分类为多个组的分类方法，所述分类方法包括：分别获取所述多个记录，所述多个记录包括变量值; 将多个记录暂时分类成多个组; 基于包含在每个暂时分类的组中的变量值，计算表示多个组中的变量值的共同度的公共值; 基于共通性值将多个记录分类为多个组; 并输出分类结果。
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公开(公告)号：US20140344207A1
公开(公告)日：2014-11-20
申请号：US14263100
申请日：2014-04-28
Applicant: Fujitsu Limited
Inventor： Koji MARUHASHI , NOBUHIRO YUGAMI
IPC: G06N7/00
CPC classification number: G06N7/005
Abstract: A computing unit obtains a graph including nodes and edges and representing a communication condition at first timing and at second timing and detects an edge that is added between the first and second timing among the edges. The computing unit calculates probabilities of transmitting information from each node to nodes coupled to the added edge, selects a subset of the nodes based on the calculated probabilities, selects nodes included in the subset as the starting points of information, calculates first probabilities of transmitting information from the selected nodes to each node based on the graph obtained at the first timing and second probabilities of transmitting information from the selected nodes to each node based on the graph obtained at the second timing, and detects a change in the communication condition between the first and second timing by comparing the first probabilities with the second probabilities.
Abstract translation: 计算单元获得包括节点和边缘并且在第一定时和第二定时处表示通信条件的图形，并且检测在边缘之间的第一和第二定时之间相加的边缘。 计算单元计算从每个节点传送信息到耦合到相邻边缘的节点的概率，基于所计算的概率选择节点的子集，选择包括在该子集中的节点作为信息的起始点，计算发送信息的第一概率 基于在第一定时获得的图形，从所选择的节点到每个节点，基于在第二定时获得的图形，从所选择的节点向每个节点发送信息的第二概率，并且检测第一 以及通过将第一概率与第二概率进行比较来进行第二定时。
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公开(公告)号：US20250005390A1
公开(公告)日：2025-01-02
申请号：US18884326
申请日：2024-09-13
Applicant: Fujitsu Limited
Inventor： Yusuke KOYANAGI , Tatsuya ASAI , Koji MARUHASHI
IPC: G06N5/025
Abstract: A non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute a process includes receiving a hypothesis to be interpreted, by using a first storage that includes, for each piece of knowledge that indicates a plurality of resources and a relationship between the resources, basis information that serves as a basis of the knowledge and a rule identifier connected with a rule used to interpret the hypothesis, acquiring the basis information and the rule identifier that correspond to the hypothesis to be interpreted, and by using a second storage that includes, for each rule identifier, a probability that the rule and the hypothesis coincide with existing knowledge, acquiring the probability of coinciding with the existing knowledge that corresponds to the acquired rule identifier.
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公开(公告)号：US20220138627A1
公开(公告)日：2022-05-05
申请号：US17464738
申请日：2021-09-02
Applicant: FUJITSU LIMITED
Inventor： Masaru TODORIKI , Koji MARUHASHI
Abstract: A machine learning method is performed by a computer. The method includes acquiring first graph information, generating second graph information, without changing a coupling state between nodes included in the first graph information, by a change process of changing an attribute value of a coupling between the nodes, and performing machine learning on a model, based on the first graph information and the second graph information.
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Patent Agency Ranking
公开(公告)号：US20210365522A1
公开(公告)日：2021-11-25
申请号：US17202400
申请日：2021-03-16
Applicant: FUJITSU LIMITED
Inventor： Kenichiroh Narita , Koji MARUHASHI
Abstract: A conversion method is performed by a computer. The method includes calculating, with respect to a core tensor and a factor matrix generated by decomposing tensor data, a rotational conversion matrix that reduces a value of an element included in the factor matrix, generating, based on the core tensor and an inverse rotational conversion matrix of the rotational conversion matrix, a core tensor after conversion obtained by converting the core tensor, and outputting the core tensor after conversion.
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17.发明申请公开(公告)号：US20190087384A1
公开(公告)日：2019-03-21
申请号：US16135446
申请日：2018-09-19
Applicant: FUJITSU LIMITED
Inventor： Keisuke GOTO , Koji MARUHASHI , Hiroya INAKOSHI
Abstract: A non-transitory computer-readable recording medium stores therein a learning data selection program that causes a computer to execute a process including: extracting a first input data group relating to first input data in correspondence with designation of the first input data included in an input data group input to a machine learning model, the machine learning model classifying or determining transformed data that is transformed from input data; acquiring a first transformed data group of the machine learning model and a first output data group of the machine learning model, respectively, the first transformed data group being input to the machine learning model and corresponding to the first input data group, the first output data group corresponding to the first transformed data group; and selecting learning target data of an estimation model from the first input data group.
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公开(公告)号：US20180096247A1
公开(公告)日：2018-04-05
申请号：US15720056
申请日：2017-09-29
Applicant: FUJITSU LIMITED
Inventor： Koji MARUHASHI
CPC classification number: G06N3/08 , G06F21/55 , G06K9/66 , G06K2209/01 , G06N3/084 , G06N20/00 , H04L63/1408
Abstract: A machine learning apparatus determines an order in which numerical values in an input dataset are to be entered to a neural network for data classification, based on a reference pattern that includes an array of reference values to provide a criterion for ordering the numerical values. The machine learning apparatus then calculates an output value of the neural network whose input-layer neural units respectively receive the numerical values arranged in the determined order. The machine learning apparatus further calculates an input error at the input-layer neural units, based on a difference between the calculated output value and a correct classification result indicated by a training label. The machine learning apparatus updates the reference values in the reference pattern, based on the input error at the input-layer neural units.
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公开(公告)号：US20180095933A1
公开(公告)日：2018-04-05
申请号：US15720998
申请日：2017-09-29
Applicant: FUJITSU LIMITED
Inventor： Koji MARUHASHI
CPC classification number: G06F15/82 , G06F15/8053 , G06F17/10 , G06F17/153 , G06F17/18 , G06F2015/763
Abstract: A data transformation apparatus selects items one by one and generates a first weight dataset and a second weight dataset on the basis of similarity between first records in a first dataset and second records in a second datasets. The first records and second records respectively include first item values and second item values that belong to the selected item. Based on the first weight dataset, the data transformation apparatus transforms the first dataset into a first similarity-determining dataset including third records. Each third record includes a numerical value that indicates a relationship between transformed item values belonging to different items. Further, based on the second weight dataset, the data transformation apparatus transforms the second dataset into a second similarity-determining dataset including fourth records. Each fourth record includes a numerical value that indicates a relationship between transformed item values belonging to different items.
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公开(公告)号：US20170154445A1
公开(公告)日：2017-06-01
申请号：US15356935
申请日：2016-11-21
Applicant: FUJITSU LIMITED
Inventor： Koji MARUHASHI
CPC classification number: G06T11/206 , G06F21/00 , G06N7/005 , H04L63/1408
Abstract: A computer generates connection matrixes corresponding to subgraphs extracted from source graphs. The connection matrixes include a plurality of elements each describing a connection between nodes in a corresponding subgraph or between a node in the corresponding subgraph and a neighboring node connected to one of the nodes in the corresponding subgraph. Based on the connection matrixes, the computer then generates a reference matrix that indicates a characteristic pattern of connections of nodes in the subgraphs, taking into consideration an order in which these nodes are arranged. The computer further performs a node-ordering swap operation on individual subgraphs, such that a submatrix representing node-to-node connections in a subgraph will be more similar to the reference matrix. The node-ordering swap operation includes changing the order of two nodes in a subgraph or swapping one node in a subgraph with a neighboring node connected to that subgraph.
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