| Autor: |
Blanco Gómez, Rosa, Inza Cano, Iñaki, Larrañaga Múgica, Pedro María |
| Jazyk: |
angličtina |
| Rok vydání: |
2004 |
| Předmět: |
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| Zdroj: |
International Journal of Intelligent System, ISSN 0884-8173, 2004-01, Vol. 18, No. 2 |
| Popis: |
The induction of the optimal Bayesian network structure is NP-hard, justifying the use of search heuristics. Two novel population-based stochastic search approaches, univariate marginal distribution algorithm (UMDA) and population-based incremental learning (PBIL), are used to learn a Bayesian network structure from a database of cases in a score search framework. A comparison with a genetic algorithm (GA) approach is performed using three different scores: penalize maximum likelihood, marginal likelihood, and information-theory– based entropy. Experimental results show the interesting capabilities of both novel approaches with respect to the score value and the number of generations needed to converge. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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