| Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2015: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2013: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Outline of Final Research Achievements |
Kernel method is one of the most important methods in machine learning. Its effectiveness depends on the kernel function and its parameter. We developed methods for optimizing the kernel function based on the data distribution of the intrinsic high dimensional space associated with the kernel function. We also proposed a similarity measure for the given data and the newly observed data based on the notion of information content in the given data, and applied the proposed measure for classifying a set of data and finding outliers from the observed dataset. We applied the developed methods to speaker recognition, hand gesture recognition, protein structure classification, and change point detection from time series data.
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