| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2015: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Outline of Final Research Achievements |
Interest in the study of deep learning (i.e., feedforward neural networks trained by error backpropagation) has grown remarkably in the last several years. Many researchers have emphasized the black box nature of neural networks. However, the fundamental issues of why and how they work well have not been clearly understood. Although it is not easy to understand the effect of pretraining the network by unsupervised learning before supervised learning, the unsupervised learning must play an important role. The model of unsupervised learning or self-organizing model usually have two dimensional structure, and it can have high dimensional structure from the view point of discovering the salient features. Here we have examined the various unsupervised algorithms appropriate for learning using both computer simulation and mathematical analysis.
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