| Presentation | 2009-01-19 Prior Knowledge-Based Stepwise Structure Learning of Bayesian Networks Hirotaka FUKUI, Daisuke KITAKOSHI, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Bayesian networks are graphical models representing stochastic dependencies among random variables and are applied to a variety of research fields such as data mining. This article proposes a stepwise method learning the structure of Bayesian network based on data and prior knowledge behind the data. Applying our method contributes to the suppression of the search space for the structure learning due to the use of prior knowledge. Besides, a suitable network structure can be acquired by employing prior knowledge which is insufficient to be applied to existing structure learning methods. Computer simulations employing both of artificial and real data are carried out to discuss the validity of our method. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Bayesian network / Prior knowledge / K2 algorithm / TPDA algorithm |
| Paper # | NC2008-91 |
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| Committee | NC |
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| Conference Date | 2009/1/12(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Neurocomputing (NC) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Prior Knowledge-Based Stepwise Structure Learning of Bayesian Networks |
| Sub Title (in English) | |
| Keyword(1) | Bayesian network |
| Keyword(2) | Prior knowledge |
| Keyword(3) | K2 algorithm |
| Keyword(4) | TPDA algorithm |
| 1st Author's Name | Hirotaka FUKUI |
| 1st Author's Affiliation | Graduate School of Science and Engineering, Nagoya Institute of Technology() |
| 2nd Author's Name | Daisuke KITAKOSHI |
| 2nd Author's Affiliation | Information Engineering, Tokyo National College of Technology |
| Date | 2009-01-19 |
| Paper # | NC2008-91 |
| Volume (vol) | vol.108 |
| Number (no) | 383 |
| Page | pp.pp.- |
| #Pages | 6 |
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