| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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)
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| Outline of Final Research Achievements |
The purpose of this study is to clarify general and theoretical aspects of statistical learning theory and inverse problems by using the spin-glass theory from statistical physics. The objective of the discipline is to infer a correct probability distribution from a limited number of observations. Hence common important theoretical questions are as follows: Clarifying the achievable theoretic limit given the limited observations; designing algorithms to achieve the limit; applying the algorithms to real-world dataset.
Our actual research processes were roughly categorized into two processes: One is to clarify the theoretical limit by utilizing the spin-glass theory; the other is to invent algorithms and apply them to neurons' firing data and natural image processing.
Our study has clarified that two commonly used frameworks, the maximum entropy principle and sparse modelling, have their own theoretical limits and have contrasting pros and cons.
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