公开(公告)号：US08396816B2

公开(公告)日：2013-03-12

申请号：US12448113

申请日：2008-01-11

申请人： Shunsuke Hirose ,  Satoshi Morinaga ,  Ryohei Fujimaki

发明人： Shunsuke Hirose ,  Satoshi Morinaga ,  Ryohei Fujimaki

IPC分类号： G06N5/00

CPC分类号： G06N99/005

摘要： Kernel functions, the number of which is set in advance, are linearly coupled to generate the most suitable Kernel function for a data classification. An element Kernel generating unit 102 generates a plurality of element Kernel functions K1-Kp by using a plurality of distance functions (distance scales) d1-dp prepared in advance.A Kernel optimizing unit 103 generates an integrated Kernel function K with which the element Kernel functions K1-Kp are linearly coupled, determines coupling coefficients to optimally separate the teacher data z, and optimizes the integrated Kernel function K.A Kernel component display unit 104 displays each of the element Kernel functions K1-Kp, its coupling coefficient, and a distance scale corresponding to each of the element kernel functions on a display device 150.

摘要翻译： 预先设置的内核函数被线性耦合以产生用于数据分类的最合适的内核函数。 元素内核生成单元102通过使用预先准备的多个距离函数（距离尺度）d1-dp来生成多个元素内核函数K1-Kp。 内核优化单元103生成一个集成的内核函数K，元素内核函数K1-Kp通过该内核函数线性耦合，确定耦合系数以最佳地分离教师数据z，并优化集成的内核函数K.内核组件显示单元104显示 元素内核函数K1-Kp，其耦合系数和对应于显示设备150上的每个元素核函数的距离标度。

公开(公告)号：US20100115241A1

公开(公告)日：2010-05-06

申请号：US12448113

申请日：2008-01-11

申请人： Shunsuke Hirose ,  Satoshi Morinaga ,  Ryohei Fujimaki

发明人： Shunsuke Hirose ,  Satoshi Morinaga ,  Ryohei Fujimaki

IPC分类号： G06F9/30

CPC分类号： G06N99/005

摘要： Kernel functions, the number of which is set in advance, are linearly coupled to generate the most suitable Kernel function for a data classification. An element Kernel generating unit 102 generates a plurality of element Kernel functions K1-Kp by using a plurality of distance functions (distance scales) d1-dp prepared in advance.A Kernel optimizing unit 103 generates an integrated Kernel function K with which the element Kernel functions K1-Kp are linearly coupled, determines coupling coefficients to optimally separate the teacher data z, and optimizes the integrated Kernel function K.A Kernel component display unit 104 displays each of the element Kernel functions K1-Kp, its coupling coefficient, and a distance scale corresponding to each of the element kernel functions on a display device 150.

摘要翻译： 预先设置的内核函数被线性耦合以产生用于数据分类的最合适的内核函数。 元素内核生成单元102通过使用预先准备的多个距离函数（距离尺度）d1-dp来生成多个元素内核函数K1-Kp。 内核优化单元103生成一个集成的内核函数K，元素内核函数K1-Kp通过该内核函数线性耦合，确定耦合系数以最佳地分离教师数据z，并优化集成的内核函数K.内核组件显示单元104显示 元素内核函数K1-Kp，其耦合系数和对应于显示设备150上的每个元素核函数的距离标度。

公开(公告)号：US20100100511A1

公开(公告)日：2010-04-22

申请号：US12523412

申请日：2008-01-16

申请人： Shunsuke Hirose ,  Kenji Yamanishi

发明人： Shunsuke Hirose ,  Kenji Yamanishi

IPC分类号： G06F15/18

CPC分类号： G06K9/00496 ,  G06N99/005

摘要： To detect a statistical change-point that appears in time-series data with a high accuracy. A first model learning section 102 learns the occurrence probability distribution of time-series data 111 as a first statistical model (for example, a latent Markov model) defined by a finite number of variables including a latent variable. In the subsequent processing, the degree of a temporal change in the probability distribution is computed for each of the probability distribution of the entire first statistical model, its partial probability distribution (the probability distribution of the latent variable and conditional probability distribution contingent on the value of the latent variable), and the probability distribution in which the above plural probability distributions are linearly-combined with a weight. The change-point of the time-series data 111 is detected on the basis of the computed degree of the change.

摘要翻译： 以高精度检测时间序列数据中出现的统计变化点。 第一模型学习部分102将时间序列数据111的发生概率分布作为由包括潜在变量的有限数量的变量定义的第一统计模型（例如潜在马尔可夫模型）来学习。 在后续处理中，对于整个第一统计模型的概率分布，其部分概率分布（潜在变量和条件概率分布的概率分布取决于值）计算概率分布的时间变化程度 的潜在变量）以及上述多个概率分布与权重线性组合的概率分布。 根据计算的变化程度来检测时间序列数据111的变化点。

公开(公告)号：US08250005B2

公开(公告)日：2012-08-21

申请号：US12523412

申请日：2008-01-16

申请人： Shunsuke Hirose ,  Kenji Yamanishi

发明人： Shunsuke Hirose ,  Kenji Yamanishi

IPC分类号： G06F15/18

CPC分类号： G06K9/00496 ,  G06N99/005

摘要： To detect a statistical change-point that appears in time-series data with a high accuracy. A first model learning section 102 learns the occurrence probability distribution of time-series data 111 as a first statistical model (for example, a latent Markov model) defined by a finite number of variables including a latent variable. In the subsequent processing, the degree of a temporal change in the probability distribution is computed for each of the probability distribution of the entire first statistical model, its partial probability distribution (the probability distribution of the latent variable and conditional probability distribution contingent on the value of the latent variable), and the probability distribution in which the above plural probability distributions are linearly-combined with a weight. The change-point of the time-series data 111 is detected on the basis of the computed degree of the change.

摘要翻译： 以高精度检测时间序列数据中出现的统计变化点。 第一模型学习部分102将时间序列数据111的发生概率分布作为由包括潜在变量的有限数量的变量定义的第一统计模型（例如潜在马尔可夫模型）来学习。 在后续处理中，对于整个第一统计模型的概率分布，其部分概率分布（潜在变量和条件概率分布的概率分布取决于值）计算概率分布的时间变化程度 的潜在变量）以及上述多个概率分布与权重线性组合的概率分布。 根据计算的变化程度来检测时间序列数据111的变化点。

公开(公告)号：US20110107155A1

公开(公告)日：2011-05-05

申请号：US12812471

申请日：2009-01-13

申请人： Shunsuke Hirose ,  Kenji Yamanishi

发明人： Shunsuke Hirose ,  Kenji Yamanishi

IPC分类号： G06F11/30 ,  G06F15/18 ,  G06F17/10

CPC分类号： H04L41/0681 ,  G06F21/552 ,  H04L41/16 ,  H04L43/0817

摘要： A network fault detection apparatus includes: data distribution learning units (2, 3, 4, and 5) that take, as input, data in which the state of the network is expressed by matrix variables of a hierarchical structure and that learn the state of the network as the probability distribution of the matrix variables, and fault detection units (6 and 7) that, based on the result of learning by the data distribution learning unit, detect, as a network fault, a state in which the probability distribution transitions from a distribution that indicates the normal state of the network to a distribution that indicates another state.

摘要翻译： 网络故障检测装置包括：数据分配学习单元（2,3,4和5），其将作为输入的网络的状态由层次结构的矩阵变量表示并且学习了 网络作为矩阵变量的概率分布，以及基于数据分布学习单元的学习结果的故障检测单元（6和7），检测作为网络故障的概率分布过渡的状态 从指示网络的正常状态到指示另一状态的分发的分发。