2013 Fiscal Year Final Research Report
Plateau Phenomena of the Learning Dynamics and Stabilities of the Local Minima of the Error Function in Machine Learning
| Project/Area Number |
21500222
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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
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| Research Institution | Tokai University |
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Principal Investigator |
OZEKI Tomoko 東海大学, 情報理工学部, 教授 (10407992)
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| Project Period (FY) |
2009-04-01 – 2014-03-31
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| Keywords | 知能情報処理 / 機械学習 / 多層パーセプトロン / 隠れマルコフモデル / 強化学習 |
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| Research Abstract |
Machine learning is one of the theories to construct the systems that can learn the data given from outside world like human brains. The algorithms of machine learning are divided into three categories such as supervised learning, unsupervised learning and reinforcement learning. In this research, we have investigated the dynamics of supervised learning and reinforcement learning and proposed some improvements. (1) We have investigated the relation between the singular structure of the parameter space of hidden Markov models and the trajectories of the learning dynamics. (2) We have proposed the reinforcement learning algorithm that can adapt to the changing environments and investigated the learning dynamics.
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