A recurrent network approach to MAP explanation
| Autor: | L.R. Medsker, C.M. Bahig, Ashraf M. Abdelbar |
|---|---|
| Rok vydání: | 2003 |
| Předmět: |
Artificial neural network
Knowledge representation and reasoning Computer science business.industry Bayesian probability Probabilistic logic Bayesian network Recurrent neural nets Machine learning computer.software_genre Set (abstract data type) Recurrent neural network Simulated annealing Genetic algorithm Artificial intelligence business computer |
| Zdroj: | Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290). |
| DOI: | 10.1109/ijcnn.2002.1007536 |
| Popis: | Bayesian belief networks (BBNs) are an increasingly popular knowledge representation for reasoning under (probabilistic) uncertainty. An important problem in BBNs is finding the best, i.e. the most probable, explanation for a given set of observations, called the evidence. In this paper, we present a recurrent neural network approach to the maximum a-posteriori (MAP) problem. We measure the performance of our approach on more than 300 pairs of belief networks and evidence sets: a combination of 23 different networks and between 10 and 21 evidence sets on each network. We find that, on average, our neural network is able to return solutions within 95% of the probability of the optimal solution. |
| Databáze: | OpenAIRE |
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