| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2013: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Outline of Final Research Achievements |
Multiclass classification problems sometimes require huge computational cost and then a major (and efficient) approach for the problem is to integrate multiple binary classifier. In this framework, we proposed a general framework of ensemble, which includes various kinds of conventional integration-based methods as special cases.
We proposed an ensemble-based method for the Multi-task problem. The proposed method is based on a combination of the Itakura-Saito distance and an extended model, rather than the conventional combination of the Kullback-Leibler divergence and statistical models. We revealed statistical properties of the proposed method and investigated validity of the proposed method.
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