| Budget Amount *help |
¥5,330,000 (Direct Cost: ¥4,100,000、Indirect Cost: ¥1,230,000)
Fiscal Year 2014: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2013: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2012: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Outline of Final Research Achievements |
It is well known that the sampling theorem plays a crucial role in the field of digital signal processing. In this work, we theoretically analyzed the sampling theory for the reproducing kernel Hilbert spaces. The sampling theorem and the separability of a target function space are two sides of the same coin. We proved that a certain continuity of a reproducing kernel corresponding to the target reproducing kernel Hilbert space guarantees the separability of the reproducing kernel Hilbert space. Recently, function estimation by multiple reproducing kernels attracts much attention in this field. We also analyzed the theoretical properties of such problems and revealed the advantages of the so-called ensemble kernel regression scheme which is a combination of the estimated functions by each reproducing kernel.
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