2012 Fiscal Year Final Research Report
Theory and applications of cross-data-type machine learning methods
| Project/Area Number |
22700289
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Statistical science
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| Research Institution | The University of Tokyo |
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Principal Investigator |
SUZUKI Taiji 東京大学, 大学院・情報理工学系研究科, 助教 (60551372)
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| Project Period (FY) |
2010 – 2012
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| Keywords | 統計的学習理論 |
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| Research Abstract |
We have investigated statistical convergence properties of Multiple Kernel Learning (MKL) with various types of regularizations. Moreover, we proposed a Bayesian variant of MKL and showed that it has optimality without strong assumptions on the design which are assumed in conventional theoretical analysis of MKL. We also proposed an online optimization method that is useful for structured regularization. It was shown that the proposed algorithm converges in the mini-max optimal rate.
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