公开(公告)号：US20170076211A1

公开(公告)日：2017-03-16

申请号：US15122461

申请日：2015-03-03

Applicant: NEC Corporation

Inventor： Yukitaka KUSUMURA ,  Ryohei FUJIMAKI ,  Yasuhiro SOGAWA ,  Satoshi MORINAGA

IPC: G06N5/04 ,  G06N99/00

CPC classification number: G06N5/04 ,  G06N7/005 ,  G06N20/00

Abstract: A feature-converting device that provides good features quickly. The device includes first and second feature construction units and first and second feature selection units. The first feature construction unit receives one or more first features and constructs one or more second features that represent the results of applying a unary function to the respective first features. The first feature selection unit computes relevance between the first and second features and a target variable that includes elements associated with elements included in the first features and selects one or more third features that represent highly relevant features. The second feature construction unit constructs one or more fourth features that represent the results of applying a multi-operand function to the third features. The second feature selection unit computes the relevance between the third and fourth features and the target variable and selects at least one fifth feature that represents highly relevant features.

Abstract translation: 功能转换设备快速提供良好的功能。 该装置包括第一和第二特征构造单元以及第一和第二特征选择单元。 第一特征构造单元接收一个或多个第一特征并构造表示将一元函数应用于相应的第一特征的结果的一个或多个第二特征。 第一特征选择单元计算第一和第二特征之间的相关性以及包括与包括在第一特征中的元素相关联的元素的目标变量，并且选择表示高度相关特征的一个或多个第三特征。 第二特征构造单元构造表示将多操作数函数应用于第三特征的结果的一个或多个第四特征。 第二特征选择单元计算第三和第四特征与目标变量之间的相关性，并且选择表示高度相关特征的至少一个第五特征。

公开(公告)号：US20160232539A1

公开(公告)日：2016-08-11

申请号：US15023986

申请日：2014-09-03

Applicant: NEC CORPORATION

Inventor： Satoshi MORINAGA ,  Ryohei FUJIMAKI

IPC: G06Q30/02

CPC classification number: G06Q30/0201 ,  G06Q30/02 ,  G06Q30/0206

Abstract: This invention helps improve the precision of data mining. This information processing device is provided with the following: a function-defining means that defines a new function by composing a plurality of functions; an attribute-generating means that applies said new function to an attribute to generate a new attribute that is the result of applying that function to that attribute; and a determining means that inputs the new attribute to an analysis engine, which executes an analysis process on the basis of the attribute, and determines whether or not information outputted by said analysis engine satisfies a prescribed requirement.

Abstract translation: 本发明有助于提高数据挖掘的精度。 该信息处理装置具有以下功能：通过组合多个功能来定义新功能的功能定义装置; 属性生成装置，其将所述新功能应用于属性以生成作为将该功能应用于该属性的结果的新属性; 以及确定装置，其将新属性输入到基于属性执行分析处理的分析引擎，并且确定所述分析引擎输出的信息是否满足规定的要求。

公开(公告)号：US20140222741A1

公开(公告)日：2014-08-07

申请号：US13758267

申请日：2013-02-04

Applicant: NEC CORPORATION

Inventor： Riki ETO ,  Ryohei FUJIMAKI ,  Satoshi MORINAGA

IPC: G06N5/02

CPC classification number: G06N7/005 ,  G06F17/18 ,  G06K9/00536 ,  G06N5/02 ,  G06N5/025

Abstract: A hierarchical latent structure setting unit 81 sets a hierarchical latent structure that is a structure in which latent variables are represented by a tree structure and components representing probability models are located at nodes of a lowest level of the tree structure. A variational probability computation unit 82 computes a variational probability of a path latent variable that is a latent variable included in a path linking a root node to a target node in the hierarchical latent structure. A component optimization unit 83 optimizes each of the components for the computed variational probability. A gating function optimization unit 84 optimizes a gating function model that is a model for determining a branch direction according to the multivariate data in a node of the hierarchical latent structure, on the basis of the variational probability of the latent variable in the node.

Abstract translation: 分层潜在结构设置单元81设置作为其中潜变量由树结构表示的结构的分层潜在结构，并且表示概率模型的分量位于树结构的最底层的节点处。 变分概率计算单元82计算作为潜在变量的路径潜变量的变分概率，所述潜变量包括在将根节点链接到分层潜在结构中的目标节点的路径中。 分量优化单元83针对所计算的变分概率优化每个分量。 门控功能优化单元84基于节点中的潜在变量的变分概率来优化门控功能模型，门控功能模型是根据层级潜在结构的节点中的多变量数据确定分支方向的模型。

公开(公告)号：US20210027109A1

公开(公告)日：2021-01-28

申请号：US17043329

申请日：2018-10-29

Applicant: NEC Corporation

Inventor： Shinji ITO ,  Ryohei FUJIMAKI

IPC: G06K9/62 ,  G06N20/00

Abstract: A learning unit 81 generates a plurality of sample groups from samples used for learning, each of the sample groups containing at least one of samples not contained in the other sample groups, and generates a plurality of prediction models using each of the generated sample groups. An optimization unit 82 generates objective functions, represented by the sum of a plurality of functions, on the basis of explained variables predicted by the prediction models and constraints for optimization, and optimizes the generated objective functions. An evaluation unit 83 evaluates a result of the optimization for each of the objective functions.

公开(公告)号：US20190279037A1

公开(公告)日：2019-09-12

申请号：US16346579

申请日：2016-11-08

Applicant: NEC Corporation

Inventor： Akira TANIMOTO ,  Yousuke MOTOHASHI ,  Ryohei FUJIMAKI

IPC: G06K9/62 ,  G06N20/00

Abstract: A multi-task relationship learning system 80 for simultaneously estimating a plurality of prediction models includes a learner 81 for optimizing the prediction models so as to minimize a function that includes a sum total of errors indicating consistency with data and a regularization term deriving sparsity relating to differences between the prediction models, to estimate the prediction models.

公开(公告)号：US20190018823A1

公开(公告)日：2019-01-17

申请号：US16069938

申请日：2017-02-01

Applicant: NEC Corporation

Inventor： Akihiro YABE ,  Ryohei FUJIMAKI

IPC: G06F17/16 ,  G06F17/50

Abstract: An information processing device according to one aspect of the present invention includes: a memory; and at least one processor coupled to the memory wherein, the processor performing operation, the operation comprising: acquiring an optimization model for calculating an optimum solution considering variation in one or more parameters; calculating the optimum solution in the optimization model; transforming the optimization model based on the optimum solution; and outputting the optimum solution.

公开(公告)号：US20180373764A1

公开(公告)日：2018-12-27

申请号：US15774645

申请日：2016-11-14

Applicant: NEC Corporation

Inventor： Yukitaka KUSUMURA ,  Ryohei FUJIMAKI

IPC: G06F17/30

Abstract: A table storage unit 81 stores a first table including an objective variable and a second table different in granularity from the first table. A descriptor creation unit 82 creates a feature descriptor for generating a feature which is a variable that can influence the objective variable, from the first table and the second table. The descriptor creation unit 82 creates a plurality of feature descriptors, each by generating a combination of a mapping condition element indicating a mapping condition for rows in the first table and the second table and a reduction method element indicating a reduction method for reducing, for each objective variable, data of each column included in the second table.

公开(公告)号：US20180299847A1

公开(公告)日：2018-10-18

申请号：US15578935

申请日：2015-09-25

Applicant: NEC CORPORATION

Inventor： Riki ETO ,  Ryohei FUJIMAKI

IPC: G05B13/04 ,  G06F17/18

Abstract: An initial value determination means 71 determines an initial value of a scheduling parameter of a target system. Furthermore, a convergence determination means 75 determines whether the value of a predetermined evaluation function has converged. Until it is determined that the value of the predetermined evaluation function has converged, a state variable calculation means 72 repeatedly calculates a value of a state variable, a regression coefficient calculation means 73 repeatedly calculates a value of a regression coefficient, and a scheduling parameter prediction model derivation means repeatedly derives a scheduling parameter prediction model and calculates the value of the scheduling parameter. When the value of the predetermined evaluation function converges, a model estimation means 76 estimates a linear parameter-varying model of the target system on the basis of the value of the state variable and the value of the scheduling parameter at that point in time.

公开(公告)号：US20170213158A1

公开(公告)日：2017-07-27

申请号：US15500477

申请日：2015-07-10

Applicant: NEC Corporation

Inventor： Yusuke MURAOKA ,  Ryohei FUJIMAKI ,  Ichirou AKIMOTO

IPC: G06N99/00 ,  G06Q50/18 ,  G06N5/02

CPC classification number: G06N20/00 ,  G06N5/022 ,  G06Q10/04 ,  G06Q30/0202 ,  G06Q50/18

Abstract: A feature calculation unit 81 calculates a feature that is likely to influence cancellation by a user based on a communication state log that indicates a communication state of a base station when the user has been engaged in communication or making a call. A learning device 82 learns a model representing a behavioral characteristic of the user by using the calculated feature as an explanatory variable. A prediction device 83 predicts the behavioral characteristic of the user using the feature generated from the communication state log and the model.

公开(公告)号：US20150120638A1

公开(公告)日：2015-04-30

申请号：US14066265

申请日：2013-10-29

Applicant: NEC Corporation

Inventor： Yusuke MURAOKA ,  Ryohei FUJIMAKI

IPC: G06N5/04

CPC classification number: G06N7/005

Abstract: A model estimation device includes: a data input unit; a state number setting unit; an initialization unit which sets initial values of a variational probability of a latent variable, a parameter, and the type of each component; a latent variable variational probability computation unit which computes the variational probability of the latent variable so as to maximize a lower bound of a marginal model posterior probability; a component optimization unit which estimates an optimal type of each component and a parameter thereof so as to maximize the lower bound of the marginal model posterior probability separated for each component of the latent variable model; an optimality determination unit which determines whether or not to continue the maximization of the lower bound of the marginal model posterior probability; and a result output unit which outputs a result.

Abstract translation: 模型估计装置包括：数据输入单元; 状态号设定单元; 初始化单元，其设定潜变量的变分概率的初始值，参数以及各成分的种类; 潜变量变异概率计算单元，其计算潜变量的变分概率，以便最大化边际模型后验概率的下限; 估计每个分量的最佳类型及其参数的分量优化单元，以便最大化潜在变量模型的每个分量分离的边际模型后验概率的下限; 确定是否继续边际模型后验概率的下限的最大化的最优性确定单元; 以及输出结果的结果输出单元。