| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Outline of Final Research Achievements |
The present study tried to extend the competitive learning methods to more generalized methods. The generalized competitive learning can be used to maximize mutual information between neurons and input patterns, disentangling complex patterns into a set of simple features. Thus, maximized mutual information can be compressed to be represented by the simplest neural networks without hidden layers. Then, it becomes easier to interpret the inference mechanism of complex neural networks by using the simplest networks. Applied to the real business data sets, it was found that the information maximization and compression could be used to create simpler and easily interpretable representations on the relations between inputs and outputs.
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