Nec Corporation

Shunsuke Hirose

Ryohei Fujimaki

Satoshi Morinaga

Kenji Yamanishi

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 unitgenerates a plurality of element Kernel functions K-Kby using a plurality of distance functions (distance scales) d-dprepared in advance. A Kernel optimizing unitgenerates an integrated Kernel function K with which the element Kernel functions K-Kare linearly coupled, determines coupling coefficients to optimally separate the teacher data z, and optimizes the integrated Kernel function K. A Kernel component display unitdisplays each of the element Kernel functions K-K, its coupling coefficient, and a distance scale corresponding to each of the element kernel functions on a display device

Kernel function generating method and device and data classification device

To detect a statistical change-point that appears in time-series data with a high accuracy. A first model learning sectionlearns the occurrence probability distribution of time-series dataas 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 datais detected on the basis of the computed degree of the change.

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Tokyo, Japan

Shunsuke Hirose