2012 Fiscal Year Final Research Report
Bayes neural networks and its application to estimation of hiddenMarkov chains
| Project/Area Number |
22500213
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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 | Aichi Medical University |
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Principal Investigator |
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| Project Period (FY) |
2010 – 2012
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| Keywords | 隠れマルコフ鎖 / 前(後)向きアルゴリズム / ベイズ判別関数 / 神経回路網 / 学習 / ロジスティック活性化関数 |
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| Research Abstract |
A hidden Markov chain is a sequence of states. Though the states cannot be observed, the signals, each of which is generated accompanying the transition of the states, can be observed. We have tried to estimate the sequence of states using a neural network from the observation. For the estimation, Bayesian discriminant functions are necessary. Our neural network can learn several Bayesian discriminant functions simultaneously if necessary. In the case of low-dimensional signals, the network successfully estimated a hidden Markov chain. The algorithm can in principle be applied to higher-dimensional signal cases.
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