| Project/Area Number |
22500212
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
|
|---|
| Research Institution | Chubu University |
|---|
Principal Investigator |
|
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| Project Period (FY) |
2010 – 2012
|
| Project Status |
Completed (Fiscal Year 2012)
|
| Budget Amount *help |
¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2012: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2011: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2010: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
|
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| Keywords | 機械学習 / 特異モデル / 探索法 / 特異領域 / 多層パーセプトロン |
|---|
| Research Abstract |
We proposed a new learning method called SSF for multilayer perceptron (MLP), which makes good use of singular regions to stably and successfully find excellent solutions commensurate with the number of hidden units. SSF worked well in our experiments using artificial and real data sets. We also proposed another learning method for MLP which utilizes eigen vector descent, and showed that it moved through flat singular regions to find excellent solutions. Moreover, we got very promising preliminary results that our SSF framework can be applied to learning of complex-valued MLP to find unbounded or periodic solutions.
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