STATISTICAL MODEL LEARNING DEVICE, STATISTICAL MODEL LEARNING METHOD, AND PROGRAM
First Claim
1. A statistical model learning device comprising:
- a data classification unit for referring to structural information generally possessed by a data which is a learning object, and extracting a plurality of subsets from the training data;
a statistical model learning unit for learning the subsets and creating statistical models respectively;
a data recognition unit for utilizing the respective statistical models to recognize other data different from the training data and acquire recognition results;
an information amount calculation unit for calculating information amounts of the other data from a degree of discrepancy of the recognition results acquired from the respective statistical models; and
a data selection unit for selecting the data with a large information amount from the other data, and adding the same to the training data.
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Abstract
A statistical model learning device is provided to efficiently select data effective in improving the quality of statistical models. A data classification means 601 refers to structural information 611 generally possessed by a data which is a learning object, and extracts a plurality of subsets 613 from the training data 612. A statistical model learning means 602 utilizes the plurality of subsets 613 to create statistical models 614 respectively. A data recognition means 603 utilizes the respective statistical models 614 to recognize other data 615 different from the training data 612 and acquires each recognition result 616. An information amount calculation means 604 calculates information amounts of the other data 615 from a degree of discrepancy among the statistical models of the recognition results. A data selection means 605 selects the data with a large information amount and adds the same to the training data 612.
35 Citations
37 Claims
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1. A statistical model learning device comprising:
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a data classification unit for referring to structural information generally possessed by a data which is a learning object, and extracting a plurality of subsets from the training data; a statistical model learning unit for learning the subsets and creating statistical models respectively; a data recognition unit for utilizing the respective statistical models to recognize other data different from the training data and acquire recognition results; an information amount calculation unit for calculating information amounts of the other data from a degree of discrepancy of the recognition results acquired from the respective statistical models; and a data selection unit for selecting the data with a large information amount from the other data, and adding the same to the training data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 37)
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13. A statistical model learning method comprising:
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referring to structural information generally possessed by a data which is a learning object, and extracting a plurality of subsets from the training data; learning the subsets and creating statistical models respectively; utilizing the respective statistical models to recognize other data different from the training data and acquire recognition results; calculating information amounts of the other data from a degree of discrepancy of the recognition results acquired from the respective statistical models; and selecting the data with a large information amount from the other data, and adding the same to the training data. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. A computer-readable medium storing a program comprising computer executable instructions for causing a computer to carry out a processing operation comprising:
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a data classification process for referring to structural information generally possessed by a data which is a learning object, and extracting a plurality of subsets from the training data; a statistical model learning process for learning the subsets and creating statistical models respectively; a data recognition process for utilizing the respective statistical models to recognize other data different from the training data and acquire recognition results; an information amount calculation process for calculating information amounts of the other data from a degree of discrepancy of the recognition results acquired from the respective statistical models; and a data selection process for selecting the data with a large information amount from the other data, and adding the same to the training data. - View Dependent Claims (26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36)
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Specification