The problem of learning Bayesian networks from statistical data is described and reformulated as a discrete optimization problem. For a solution we employ the stochastic algorithm that is known as simulated annealing and that is based on the Markov Chain Monte Carlo approach. Numerical examples are
✦ LIBER ✦
Simulated annealing technique for fast learning of SOM networks
✍ Scribed by Antonino Fiannaca, Giuseppe Di Fatta, Riccardo Rizzo…
- Book ID
- 120913641
- Publisher
- Springer-Verlag
- Year
- 2011
- Tongue
- English
- Weight
- 601 KB
- Volume
- 22
- Category
- Article
- ISSN
- 0941-0643
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