Patent application number | Description | Published |
20090306982 | APPARATUS, METHOD AND PROGRAM FOR TEXT MINING - Disclosed is an apparatus includes a text input device that inputs text data provided with confidence measure, as subject for mining, a language processing unit that performs language analysis of the input text data provided with the confidence measures, a confidence measure exploiting characteristic word count unit that counts the characteristic words in the input text to provide a count result and that exploits the statistical information and the confidence measures provided in the input text to correct the count result obtained, a characteristic measure calculation unit that calculates the characteristic measure of each characteristic word from the corrected count result, a mining result output device that outputs the characteristic measure of each characteristic word obtained, a user operation input device for a user to input setting for language processing of the input text and setting for a technique for calculating the characteristic measure being found, a mining process management unit that transmits a user's command delivered from the user operation input device to respective components, and a statistical information database that records and holds the statistical information representing the property of the input text that may be presupposed. | 12-10-2009 |
20100115241 | KERNEL FUNCTION GENERATING METHOD AND DEVICE AND DATA CLASSIFICATION DEVICE - 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 | 05-06-2010 |
20110004578 | ACTIVE METRIC LEARNING DEVICE, ACTIVE METRIC LEARNING METHOD, AND PROGRAM - A metric application unit receives data under analysis having a plurality of attributes and a metric indicative of the distance between the data under analysis, calculates the distance between the data under analysis, and output and stores a data analysis result which is generated from an analysis on the data under analysis with a predetermined function, using the calculated distance between the data under analysis. A metric optimization unit generates side-information based on an indication of feedback information entered from the outside and including either similarities between the data under analysis, or the attributes, or a combination thereof, generates a metric which complies with a predetermined condition, based on the generated side information, and stores the generated metric in a metric learning result storage unit. | 01-06-2011 |
20110231350 | ACTIVE METRIC LEARNING DEVICE, ACTIVE METRIC LEARNING METHOD, AND ACTIVE METRIC LEARNING PROGRAM - An active metric learning device includes a metric application data analysis unit, a metric optimization unit, and an attribute clustering unit. The metric application data analysis unit is formed with a metric applying module for calculating the distance between data to be analyzed, a data analyzing module for analyzing the data using a predetermined function and the distances between the data to be analyzed and outputting the result of the data analysis, and an analysis result storage unit for storing the result of the data analysis. The metric optimization unit is formed with a feedback converting module for creating side information according to the command of feedback from the user and a metric learning module for generating a metric matrix optimized under a predetermined condition using the created side information. The attribute clustering unit clusters the metric matrix optimized by the metric optimization unit and structuralizes the attributes. | 09-22-2011 |
20120278126 | LOSS DISTRIBUTION CALCULATION SYSTEM, LOSS DISTRIBUTION CALCULATION METHOD AND LOSS DISTRIBUTION CALCULATION-USE PROGRAM - Provided is a loss distribution calculation system, comprising: a frequency distribution/scale distribution input section that inputs information about a frequency distribution and a scale distribution; a scale distribution discretization section that performs either one or both of upside and downside discretizations for the input scale distribution; a sub-composite distribution calculation section that calculates, after performing division of all events, a probability value of a cumulative sum of losses for a portion out of all events in order to calculate either one or both of an upside sub-composite distribution and a downside sub-composite distribution, the upside sub-composite distribution being calculated based on the frequency distribution and the upside-discretized scale distribution, and the downside sub-composite distribution being calculated based on the frequency distribution and the downside-discretized scale distribution; an accuracy calculation section that calculates upper and lower bounds of a loss distribution function based on either one or both of the upside and downside sub-composite distributions, calculates a function, as an approximate value of the loss distribution function, based on either one or both of the upside and downside sub-composite distributions, and calculates an accuracy of the approximate value; and a loss distribution output section that outputs information about the approximate value of the loss distribution function with guaranteed accuracy represented by information about the calculated accuracy. | 11-01-2012 |
20130204810 | DISCRIMINANT MODEL LEARNING DEVICE, METHOD AND PROGRAM - 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. | 08-08-2013 |
20130204811 | OPTIMIZED QUERY GENERATING DEVICE AND METHOD, AND DISCRIMINANT MODEL LEARNING METHOD - 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. | 08-08-2013 |
20130211801 | MULTIVARIATE DATA MIXTURE MODEL ESTIMATION DEVICE, MIXTURE MODEL ESTIMATION METHOD, AND MIXTURE MODEL ESTIMATION PROGRAM - 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. A mixture model estimation device comprises: a data input unit to which data of a mixture model to be estimated, candidate values of the number of mixtures which are required for estimating the mixture model of the data, and types of components configuring the mixture model and parameters thereof, are input; a processing unit which sets the number of mixtures from the candidate values, calculates, with respect to the set number of mixtures, a variation probability of a hidden variable for a random variable which becomes a target for mixture model estimation of the data, and estimates the optimal mixture model by optimizing the types of the components and the parameters therefor using the calculated variation probability of the hidden variable so that the lower bound of the posterior probabilities of the model separated for each component of the mixture model can be maximized; and a model estimation result output unit which outputs the model estimation result obtained by the processing unit. | 08-15-2013 |
20130311231 | RISK MANAGEMENT DEVICE - A risk management device includes: a memory for storing a plurality of verification units each composed of one or more scenario data each including a predicted value of loss occurrence frequency, a verification range that is a collection of the plurality of verification units, and actual loss occurrence numbers corresponding to the scenario data; and a processor connected to the memory. The processor is programmed to determine by using a goodness-of-fit test on a Poisson distribution whether the total value of the loss occurrence numbers corresponding to the scenario data included in the verification range follows a Poisson distribution that the total value of predicted values of loss occurrence frequency in the scenario data included in the verification range is defined as a mean. | 11-21-2013 |
20130318623 | RISK-MANAGEMENT DEVICE - A risk management device includes a memory for storing actual total loss amounts of N periods, and a processor connected to this memory. The processor is programmed to determine whether actual levels showing confidence intervals of actual total loss amounts in a total loss amount distribution calculated by the risk weighing device follow a uniform distribution on an interval [0,1], by a goodness-of-fit test using order statistics for a uniform distribution. | 11-28-2013 |
20130332225 | RISK-PROFILE GENERATION DEVICE - A risk profile generation device includes: a memory for storing model information of a risk profile defined by a first parameter set, model information of a probability distribution of the first parameter set defined by a second parameter set, and a required condition; and a processor connected to this memory. The processor is configured to: calculate a value of the second parameter set such that a risk profile to be specified by applying a value of the first parameter set generated in accordance with the probability distribution to the model information of the risk profile satisfies the required condition with a higher probability; and generate a value of the first parameter set in accordance with a probability distribution specified by applying this calculated value of the second parameter set to the model information of the probability distribution. | 12-12-2013 |
20130346288 | RISK-PROFILE GENERATION DEVICE - A risk profile generation device includes: a memory for storing model information of a risk profile defined by a first parameter set, model information of a probability distribution of the first parameter set defined by a second parameter set, a plurality of required conditions, and weighting factors; and a processor is configured to: calculate a value of the second parameter set such that a risk profile to be specified by applying a value of the first parameter set generated based on the probability distribution to the model information of the risk profile satisfies the required conditions with a higher probability, for the required conditions; generate a probability distribution of the first parameter set from the calculated value of the second parameter set, the weighting factors, and the model information of the probability distribution; and generate a value of the first parameter set based on the generated probability distribution. | 12-26-2013 |
20140012621 | RISK MANAGEMENT DEVICE - A storing unit stores loss data each including a loss amount and loss occurrence frequency, and a coefficient table holding a coefficient in association with the loss occurrence frequency, the coefficient being equal to a value of an occurrence number at a lower α % point (α is a predetermined constant) in a cumulative distribution function of a probability distribution with the loss occurrence frequency as a parameter. A processor is programmed to calculate multiplication values, each of which is calculated for each of the loss data and is a multiplication value of the coefficient held in the coefficient table in association with the loss occurrence frequency included in the loss data and the loss amount included in the loss data. | 01-09-2014 |
20140114890 | PROBABILITY MODEL ESTIMATION DEVICE, METHOD, AND RECORDING MEDIUM - In order to learn an appropriate probability model in a probability model learning problem where a first issue and a second issue manifest concurrently by solving the two at the same time, provided is a probability model estimation device for obtaining a probability model estimation result from first to T-th (T≧2) training data and test data. The probability model estimation device includes: first to T-th training data distribution estimation processing units for obtaining first to T-th training data marginal distributions with respect to the first to the T-th training models, respectively; a test data distribution estimation processing unit for obtaining a test data marginal distribution with respect to the test data; first to T-th density ratio calculation processing units for calculating first to T-th density ratios, which are ratios of the test data marginal distribution to the first to the T-th training data marginal distributions, respectively; an objective function generation processing unit for generating an objective function that is used to estimate a probability model from the first to the T-th density ratios; and a probability model estimation processing unit for estimating the probability model by minimizing the objective function. | 04-24-2014 |
20140214747 | MULTIVARIATE DATA MIXTURE MODEL ESTIMATION DEVICE, MIXTURE MODEL ESTIMATION METHOD, AND MIXTURE MODEL ESTIMATION PROGRAM - 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. A mixture model estimation device comprises: a data input unit to which data of a mixture model to be estimated, candidate values of the number of mixtures which are required for estimating the mixture model of the data, and types of components configuring the mixture model and parameters thereof, are input; a processing unit which sets the number of mixtures from the candidate values, calculates, with respect to the set number of mixtures, a variation probability of a hidden variable for a random variable which becomes a target for mixture model estimation of the data, and estimates the optimal mixture model by optimizing the types of the components and the parameters therefor using the calculated variation probability of the hidden variable so that the lower bound of the posterior probabilities of the model separated for each component of the mixture model can be maximized; and a model estimation result output unit which outputs the model estimation result obtained by the processing unit. | 07-31-2014 |
20140222741 | HIERARCHICAL LATENT VARIABLE MODEL ESTIMATION DEVICE, HIERARCHICAL LATENT VARIABLE MODEL ESTIMATION METHOD, AND RECORDING MEDIUM - A hierarchical latent structure setting unit | 08-07-2014 |
20140236869 | INTERACTIVE VARIABLE SELECTION DEVICE, INTERACTIVE VARIABLE SELECTION METHOD, AND INTERACTIVE VARIABLE SELECTION PROGRAM - An optimality degree computation unit computes an optimality degree in the case where a first variable included in a variable set is a candidate for an addition variable, using an objective function. An addition threshold computation unit computes an addition threshold based on the computed optimality degree, the addition threshold being a threshold of the optimality degree and indicating a criterion for determining whether or not the first variable is to be set as the candidate for the addition variable. An objective function value computation unit computes an objective function value which is a difference between a value of the objective function computed using variables to be optimized and a value of the objective function computed using the variables to be optimized from which a second variable included in a nonzero variable set is excluded. | 08-21-2014 |
20140244551 | INFORMATION SPREAD SCALE PREDICTION DEVICE, INFORMATION SPREAD SCALE PREDICTION METHOD, AND INFORMATION SPREAD SCALE PREDICTION PROGRAM - To provide an information spread scale prediction device capable of accurately predicting the number of future contributions for a specific topic in SNS and the like. The information spread scale prediction device includes: a learning text data input unit which acquires learning text data from a specific website; a node influence learning unit which calculates the influence for the number of statements by each group to which a node specifying a single specific user belongs for the topic from the number of statements by each classified topic, and stores it as learning data; a prediction text data input unit which acquires prediction text data from the specific website after storing the learning data; and a future contribution number prediction unit which predicts and outputs the number of contributions at a specific future time of the topic based on the number of statements of each topic and the learning data. | 08-28-2014 |
20140297359 | RISK MANAGEMENT DEVICE - A risk management device includes: a memory for storing risk weighing data and a threshold risk amount, the risk weighing data including a plurality of scenario data each including a combination of a loss event content, loss occurrence frequency and a loss amount; and a processor. The processor is programmed to calculate post-change risk weighing data obtained by changing only loss occurrence frequency or a loss amount of one specific scenario data of the risk weighing data and transmit to a risk weighing device, and repeatedly executing a process of comparing a risk amount received from the risk weighing device with the threshold risk amount while changing a change width of the loss occurrence frequency or the loss amount, thereby performing calculation of an increase rate of the loss occurrence frequency or the loss amount of the specific scenario data such that a risk amount reaches the threshold risk amount. | 10-02-2014 |
20150088789 | HIERARCHICAL LATENT VARIABLE MODEL ESTIMATION DEVICE, HIERARCHICAL LATENT VARIABLE MODEL ESTIMATION METHOD, SUPPLY AMOUNT PREDICTION DEVICE, SUPPLY AMOUNT PREDICTION METHOD, AND RECORDING MEDIUM - A hierarchical latent structure setting unit | 03-26-2015 |
20150088804 | HIERARCHICAL LATENT VARIABLE MODEL ESTIMATION DEVICE, HIERARCHICAL LATENT VARIABLE MODEL ESTIMATION METHOD, AND RECORDING MEDIUM - A hierarchical latent structure setting unit | 03-26-2015 |