Abstract: A parallel allocation calculating unit calculates a parallel allocation candidate which is an element candidate in target data allocated per processing performed in parallel. A parallel calculation amount estimation processing unit estimates the calculation amount required for parallel processing when a parallel allocation candidate is allocated, based on a nonzero element count in the target data. An optimality decision processing unit decides whether or not the parallel allocation candidate is optimal based on the calculated calculation amount, and allocates the optimal element per processing performed in parallel.

Abstract: To provide a latent variable model estimation apparatus capable of implementing the model selection at high speed even if the number of model candidates increases exponentially as the latent state number and the kind of the observation probability increase. A variational probability calculating unit 71 calculates a variational probability by maximizing a reference value that is defined as a lower bound of an approximation amount, in which Laplace approximation of a marginalized log likelihood function is performed with respect to an estimator for a complete variable. A model estimation unit 72 estimates an optimum latent variable model by estimating the kind and a parameter of the observation probability with respect to each latent state. A convergence determination unit 73 determines whether a reference value, which is used by the variational probability calculating unit 71 to calculate the variational probability, converges.

Abstract: A separation surface set storage part stores information defining a plurality of separation surfaces which separate a feature space into at least one known class region respectively corresponding to at least one known class and an unknown class region. Each of the at least one known class region is separated from outside region by more than one of the plurality of separation surfaces which do not intersect to each other. A data classification apparatus determine a classification of a classification target data whose inner product in the feature space is calculable by calculating to which region of the at least one known class region and the unknown class region determined by the information stored in the separation surface set storage part the classification target data belongs. A method and apparatus for data classification which can simultaneously perform identification and outlying value classification with high reliability in a same procedure are provided.

Abstract: An abnormality information creating means creates at least one or more abnormality information which is information indicating abnormality of each data based on specificity of medical data. A side effect detecting means decides a likelihood of a side effect indicated by the abnormality information according to a predetermined rule, and detects abnormality information the likelihood of which satisfies conditions set in advance as information indicating the side effect. When receiving an input of information used to create the abnormality information as the feedback information, the abnormality information creating means creates the abnormality information based on the information. Further, when receiving as the feedback information an input of the information used to detect the side effect, the side effect detecting means detects the side effect based on the information.

Abstract: With respect to the model selection issue of a mixture model, the present invention performs high-speed model selection under an appropriate standard regarding the number of model candidates which exponentially increases as the number and the types to be mixed increase.

Abstract: To provide a discriminant model learning device capable of efficiently learning a discriminant model on which domain knowledge indicating user's knowledge or analysis intention for a model is reflected while keeping fitting to data. A query candidate storage means 81 stores candidates of a query as a model to be given with domain knowledge indicating a user's intention. A regularization function generation means 82 generates a regularization function indicating compatibility with domain knowledge based on the domain knowledge to be given to the query candidates. A model learning means 83 learns a discriminant model by optimizing a function defined by a loss function and the regularization function predefined per discriminant model.

Abstract: There is provided a multidimensional data visualization apparatus capable of visualizing the distribution of data in an input space of high-dimensional data so that the relationships between input dimensions can be seen. Subplot generation means 71 generates, from input multidimensional data, multiple subplots as charts representing data on some dimensions in the multidimensional data. Feature value calculation means 72 calculates the feature value of a relationship between paired subplots for each pair of subplots. Coordinate calculation means 73 calculates the coordinates, at which each subplot is placed, based on the feature value calculated by the feature value calculation means 72.

Abstract: To provide an optimized query generating device capable of generating an optimized query to be given with domain knowledge when generating a discriminant model on which the domain knowledge indicating user's knowledge or analysis intention for a model is reflected. A query candidate storage means 86 stores candidates of a query which is a model to be given with domain knowledge indicating a user's intention. An optimized query extraction means 87 extracts queries having low uncertainty of a discriminant model estimated by queries given with domain knowledge when the domain knowledge is given thereto from query candidates.

Abstract: An abnormality score calculating means calculates abnormality scores which are information indicating abnormality of medical data, based on specificity of the medical data. An abnormality score vector generating means creates at least one or more abnormality score vectors which are information obtained by integrating the abnormality scores. Further, a side effect detecting means which decides a likelihood of a side effect indicated by the abnormality score vector, based on a predetermined rule, and detects an abnormality score vector the likelihood of which is set in advance and which satisfies conditions as information indicating the side effect.

Abstract: Disclosed is an apparatus that generates automatically a characteristic pattern in time series data by clustering a plurality of time series subsequences generated from the time series data. The apparatus includes a time series subsequence generation unit that generates a plurality of time series subsequences from the time series data, a phase alignment unit that aligns a phase of the generated time series subsequence, a clustering unit that performs clustering of a plurality of the time series subsequences, each having a phase aligned, a storage apparatus that stores the pattern obtained by the clustering, and an output apparatus that outputs the stored pattern.

Abstract: 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.

Abstract: A malfunction analysis apparatus (100) is provided with a malfunction-analysis processor (107), an attribute-extraction processor (108), and an outputter (105). The malfunction-analysis processor (107) obtains a malfunction-contribution degree, which indicates a degree that individual malfunctions (to be called malfunctioning elements, hereafter) contribute to the malfunctioning of the object being analyzed, on the basis of the relative relationship between the data to be analyzed that has, as elements thereof, values generated on the basis of a plurality of indicator values of the object being analyzed, and representative values for the plurality of indicators corresponding to each of the plurality of malfunctions. Then, the malfunctioning elements being generated is specified, on the basis of the obtained malfunction-contribution degree.

Abstract: A diagnostic device detects a fault and estimates its cause based on the degree of change of measured data attributes. A diagnostic object change degree pattern (CDP) generation unit calculates the degree of change of each attribute of data, including attributes of the object being diagnosed measured from the object being diagnosed to generate a diagnostic object CDP which is a combination of values of the degree of change of the respective attributes. A criterion CDP memory holds in store a criterion CDP, formed of a pattern of values of the degree of change of the attributes of data measured from the object being diagnosed, in association with event(s)-to-be-diagnosed on the fault sort basis or on the fault cause basis. A CDP diagnosis unit effects pattern matching between the diagnostic object CDP and the criterion CDP in the criterion CDP memory to diagnose the object being diagnosed.

Abstract: The model selection device comprises a model optimization unit which optimizes a model for a mixed distribution, wherein related to an information criterion of complete data, with respect to a hidden variable post-event distribution of the complete data, the model optimization unit optimizes an expected information criterion of the complete data for a pair of a model and a parameter of a component which satisfies a predetermined condition.

Abstract: The present invention provides a diagnostic system that diagnoses a diagnostic object by applying a diagnostic rule to data measured on the diagnostic object wherein an object of the present invention is to allow the diagnostic rule to be updated based on a variety of diagnostic case data. Each of multiple diagnostic devices 101 makes a diagnosis by applying a diagnostic rule to diagnostic object data measured on a diagnostic object 104 and sends diagnostic case data, which includes diagnostic object data and its diagnostic result, to a diagnostic rule generation device 102 via a network 103. The diagnostic rule generation device 102 generates a diagnostic rule based on the diagnostic case data received from the multiple diagnostic devices 101 and sends the generated diagnostic rule to the diagnostic devices 101 via the network 103. The diagnostic devices 101 update a diagnostic rule in their devices with the diagnostic rule received from the diagnostic rule generation device 102.

Abstract: A separation surface set storage part stores information defining a plurality of separation surfaces which separate a feature space into at least one known class region respectively corresponding to at least one known class and an unknown class region. Each of the at least one known class region is separated from outside region by more than one of the plurality of separation surfaces which do not intersect to each other. A data classification apparatus determine a classification of a classification target data whose inner product in the feature space is calculable by calculating to which region of the at least one known class region and the unknown class region determined by the information stored in the separation surface set storage part the classification target data belongs. A method and apparatus for data classification which can simultaneously perform identification and outlying value classification with high reliability in a same procedure are provided.

Abstract: A diagnostic device which detects a fault and estimates its cause on the basis of the degree of variation of each attribute of data containing a plurality of attributes obtained by measuring a diagnosis subject. A diagnosis subject variation degree pattern generation means (110) calculates the degree of variation of each attribute of the diagnosis subject data containing the attributes obtained by measuring the diagnosis subject and generates a diagnosis subject variation degree pattern which is a combination of the degrees of variation of the attributes. A reference variation degree pattern storage device (150) stores a reference variation degree pattern composed of the degrees of variation of the attributes in association with the type of the fault and the diagnosis event of the type of the cause.

Abstract: Disclosed is an apparatus that generates automatically a characteristic pattern in time series data by clustering a plurality of time series subsequences generated from the time series data. The apparatus includes a time series subsequence generation unit that generates a plurality of time series subsequences from the time series data, a phase alignment unit that aligns a phase of the generated time series subsequence, a clustering unit that performs clustering of a plurality of the time series subsequences, each having a phase aligned, a storage apparatus that stores the pattern obtained by the clustering, and an output apparatus that outputs the stored pattern.

Abstract: 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.

Abstract: The present invention provides a diagnostic system that diagnoses a diagnostic object by applying a diagnostic rule to data measured on the diagnostic object wherein an object of the present invention is to allow the diagnostic rule to be updated based on a variety of diagnostic' case data. Each of multiple diagnostic devices 101 makes a diagnosis by applying a diagnostic rule to diagnostic object data measured on a diagnostic object 104 and sends diagnostic case data, which includes diagnostic object data and its diagnostic result, to a diagnostic rule generation device 102 via a network 103. The diagnostic rule generation device 102 generates a diagnostic rule based on the diagnostic case data received from the multiple diagnostic devices 101 and sends the generated diagnostic rule to the diagnostic devices 101 via the network 103. The diagnostic devices 101 update a diagnostic rule in their devices with the diagnostic rule received from the diagnostic rule generation device 102.