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    PREDICTION FUNCTION CREATION DEVICE, PREDICTION FUNCTION CREATION METHOD, AND COMPUTER-READABLE STORAGE MEDIUM
    1.
    发明申请
    PREDICTION FUNCTION CREATION DEVICE, PREDICTION FUNCTION CREATION METHOD, AND COMPUTER-READABLE STORAGE MEDIUM 审中-公开
    预测功能创建装置，预测功能创建方法和计算机可读存储介质

    公开(公告)号：US20160117588A1

    公开(公告)日：2016-04-28

    申请号：US14895023

    申请日：2014-06-06

    Applicant: NEC CORPORATION

    Inventor： Yusuke MURAOKA ,  Yukitaka KUSUMURA ,  Hironori MIZUGUCHI ,  Dai KUSUI

    IPC: G06N5/02 ,  G06N99/00

    CPC classification number: G06N5/02 ,  G06N20/00 ,  G06Q10/06375

    Abstract: The prediction function creation device according to the present invention for creating a prediction function to derive an objective variable by using a set of samples that include explanatory variables and an objective variable, the device includes: a clustering unit that clusters the respective samples by giving labels, and assigns weights to each label in accordance with patterns of missing values for the explanatory variables in labeled samples; a child model creation unit that makes portions of the training data partial training data on the basis of the weights, and determines an explanatory variable that constitutes the prediction function on the basis of patterns of missing values for the explanatory variables in the samples; and a mixture model creation unit that creates the prediction function with respect to each pattern of missing values by using the explanatory variable and the determined partial training data.

    Abstract translation: 根据本发明的预测函数创建装置，用于通过使用包括解释变量和目标变量的一组样本来创建预测函数以导出目标变量，所述设备包括：聚类单元，通过给出标签来聚集各个样本 ，并根据标记样本中的解释变量的缺失值模式为每个标签分配权重; 子模型创建单元，其基于权重构成训练数据部分训练数据的部分，并且基于样本中的解释变量的缺失值的模式来确定构成预测函数的解释变量; 以及混合模型创建单元，其通过使用所述解释变量和所确定的部分训练数据来创建关于缺失值的每个模式的预测函数。

    MODEL ESTIMATION DEVICE AND MODEL ESTIMATION METHOD
    2.
    发明申请
    MODEL ESTIMATION DEVICE AND MODEL ESTIMATION METHOD 有权
    模型估计装置和模型估计方法

    公开(公告)号：US20150120254A1

    公开(公告)日：2015-04-30

    申请号：US14066281

    申请日：2013-10-29

    Applicant: NEC Corporation

    Inventor： Yusuke MURAOKA ,  Ryohei FUJIMAKI

    IPC: G06F17/50

    CPC classification number: G06F17/50 ,  G06F17/18 ,  G06K9/6226 ,  G06K9/6278 ,  G06N7/005

    Abstract: A model estimation device includes: a data input unit 101; a state number setting unit; an initialization unit; a latent variable variational probability computation unit which computes a variational probability of a latent variable so as to maximize a lower bound of a model posterior probability limited in degree of freedom; 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 model posterior probability limited in degree of freedom and separated for each component of a latent variable model; a free parameter selection variable computation unit which computes the free parameter selection variable; an optimality determination unit which determines whether or not to continue the maximization of the lower bound of the model posterior probability; and a result output unit.

    Abstract translation: 模型估计装置包括：数据输入单元101; 状态号设定单元; 一个初始化单元; 潜变量变异概率计算单元，其计算潜变量的变分概率，以便最大限度地限制自由度的模型后验概率的下界; 估计每个分量的最佳类型及其参数的分量优化单元，以便最大限度地限制自由度的模型后验概率的下限并对潜变量模型的每个分量分离; 自由参数选择变量计算单元，其计算自由参数选择变量; 确定是否继续最大化模型后验概率的下限的最优性确定单元; 和结果输出单元。

    MODEL ESTIMATION DEVICE, MODEL ESTIMATION METHOD, AND INFORMATION STORAGE MEDIUM
    4.
    发明申请
    MODEL ESTIMATION DEVICE, MODEL ESTIMATION METHOD, AND INFORMATION STORAGE MEDIUM 有权
    模型估计装置，模型估计方法和信息存储介质

    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。

    INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING SYSTEM, INFORMATION PROCESSING METHOD, AND RECORDING MEDIUM
    7.
    发明申请
    INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING SYSTEM, INFORMATION PROCESSING METHOD, AND RECORDING MEDIUM 审中-公开

    公开(公告)号：US20180267934A1

    公开(公告)日：2018-09-20

    申请号：US15755214

    申请日：2016-09-07

    Applicant: NEC Corporation

    Inventor： Ryohei FUJIMAKI ,  Yusuke MURAOKA

    IPC: G06F17/11 ,  G06F17/16 ,  G06Q10/04

    CPC classification number: G06F17/11 ,  G06F17/16 ,  G06Q10/04 ,  G06Q10/063

    Abstract: An information processing device according to the present invention includes: a problem generator that, based on a first optimization problem, a lower-dimensional expression that is an expression for approximating uncertain data for the first optimization problem at a lower dimension than a dimension of the uncertain data, and a first data region that is a region of the uncertain data, generates a second optimization problem into that the first optimization problem is transformed in such a way that the second optimization problem relates to the lower-dimensional expression, and a second data region into that the first data region is transformed; and a problem solver that computes an optimum solution to the second optimization problem by using the second data region.

    VALIDATION SYSTEM, VALIDATION EXECUTION METHOD, AND VALIDATION PROGRAM
    8.
    发明申请
    VALIDATION SYSTEM, VALIDATION EXECUTION METHOD, AND VALIDATION PROGRAM 审中-公开

    公开(公告)号：US20200042924A1

    公开(公告)日：2020-02-06

    申请号：US16339942

    申请日：2017-09-08

    Applicant: NEC CORPORATION

    Inventor： Yusuke MURAOKA ,  Ryohel FUJIMAKI

    IPC: G06Q10/06 ,  G06Q10/04 ,  G06Q10/10

    Abstract: In a case where data including an input, first operation executed onto the input, and a first result obtained by the first operation is defined as validation data and data used in an evaluation target period is defined as test data, a density relation estimating unit 81 estimates a relationship between a density of a pair including an input of the validation data and the first operation onto the input and a density of the pair including an input of the test data and second operation to be executed onto the input. An expected result estimating unit 82 estimates a second result expected to be obtained by executing the second operation onto the input of the test data on the basis of the first result included in the validation data and the estimated relationship.

    PREDICTION SYSTEM AND PREDICTION METHOD
    10.
    发明申请
    PREDICTION SYSTEM AND PREDICTION METHOD 审中-公开

    公开(公告)号：US20170140401A1

    公开(公告)日：2017-05-18

    申请号：US15323280

    申请日：2015-06-04

    Applicant: NEC Corporation

    Inventor： Yusuke MURAOKA ,  Ryohei FUJIMAKI

    IPC: G06Q30/02 ,  G06N7/00

    CPC classification number: G06Q30/0202 ,  G06N7/005 ,  G06N20/00

    Abstract: From learning data that expresses inter-node connection relationships that are expressed as a graph structure or a network structure, a vicinal node information acquisition unit 81 acquires edge information that indicates the connection relationship between one node and another node to which the one node connects. Using the acquired edge information and node feature information that indicates the features of the other node, a feature value calculation unit 82 calculates a feature value that is for the one node and that is to be used for prediction.

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