2012 Fiscal Year Final Research Report
Fast Likelihood Ratio Optimization Based Upon Genaralized Logarithm and Its Applications
| Project/Area Number |
22656088
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Single-year Grants |
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| Research Field |
Communication/Network engineering
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| Research Institution | Waseda University |
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Principal Investigator |
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| Project Period (FY) |
2010 – 2012
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| Keywords | 信号処理 |
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| Research Abstract |
Likelihood optimization for learning algorithms was generalized by using the alpha-logarithm. This generalization led to a faster convergence than traditional methods. Algorithms on hidden Markov model estimation and independent component analysis were chosen since they have high ramifications. The use of the alpha-logarithm appears as the utilization of past information via momentum terms. This property enabled faster convergence than traditional methods.
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Research Products
(14 results)
