| Presentation | 2011-07-01 Covariance structure analysis by new algorithm that uses back Euler method Taichi UEYAMA, Hisashi AOMORI, Mamoru TANAKA, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The covariance structure analysis has the advantage that make it visible to phenomena that cannot be expressed numerically. The result that using software of the covariance structure analysis is depending on an initial value and the model dependencyHere, it proposes a new algorithm that introduces the back Euler method. By using a new algorithm, it is an aim to weaken an initial value and the model dependency. In this research, the domination of a new algorithm was shown by analyzing the result of the questionnaire in the covariance structure analysis. The comparison that uses SDP method and free software R is being researched. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Data mining / Covariance structure analysis / Statistics / Algorithm |
| Paper # | NLP2011-50 |
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| Conference Information | |
| Committee | NLP |
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| Conference Date | 2011/6/23(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Nonlinear Problems (NLP) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Covariance structure analysis by new algorithm that uses back Euler method |
| Sub Title (in English) | |
| Keyword(1) | Data mining |
| Keyword(2) | Covariance structure analysis |
| Keyword(3) | Statistics |
| Keyword(4) | Algorithm |
| 1st Author's Name | Taichi UEYAMA |
| 1st Author's Affiliation | Faculty Of Science and Technology, Informatics, Graduate School Of Sophia University Mamoru Tanaka laboratory() |
| 2nd Author's Name | Hisashi AOMORI |
| 2nd Author's Affiliation | / |
| 3rd Author's Name | Mamoru TANAKA |
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| Date | 2011-07-01 |
| Paper # | NLP2011-50 |
| Volume (vol) | vol.111 |
| Number (no) | 106 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |