| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Outline of Final Research Achievements |
In this research, firstly, we proposed an optimization scheme for regularized empirical risk minimization that includes SVM and logistic regression. we have shown that this scheme that performs optimization by operating multiple processes asynchronously allows efficient distributed optimization from both theoretical aspects nd experimental aspect.
Secondly, focusing on sparse learning that originally requires several tera-bytes of data, we proposed an optimization scheme that works efficiently by suppressing the amount of data. We have shown that the proposed method can extract features efficiently by using efficient data structure such as suffix array in cases in which substrings are used as features of datasets such as text and DNA.
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