| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Outline of Final Research Achievements |
In this research, a quasi-linear support vector machine (SVM) is proposed. The quasi-linear SVM, on one hand, can be seen as a nonlinear SVR model with easy-to-use structure; on the other hand, it is a nonlinear SVM with data-dependent kernel, which can composed by using machine learning methods, kernel learning methods and even deep kernel learning methods. The quasi-linear SVM is applied to switching adaptive control and high-performance pattern recognition.
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