| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Outline of Final Research Achievements |
We obtained the following two results in kernel-based machine learning.
1. We analyzed the theoretical limit of conventional multiple kernel learning, and clarified that (a) the optimal solution based on an ordinary 2-norm criterion can not achieve the theoretical limit of the multiple kernel model, and (b) the optimal solution is identical to the solution based on a certain single kernel model.
2. We developed a novel framework of kernel-based learning in which the autocorrelation prior of the unknown true function is taken into account, and also clarified that the autocorrelation function itself is the best kernel in terms of the expectation.
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