Bayesian Network Structure Learning
Language: English
Published by LAP LAMBERT Academic Publishing Jun 2018, 2018
- Softcover
- New
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Condition: New
£ 31.02
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This item is printed on demand - Print on Demand Titel. Neuware -Structure learning is a very important problem in the field of Bayesian networks (BNs). It is also an active research area for more than two decades; therefore, many approaches have been proposed in order to find an optimal structure based on training samples. In this book, we shortly introduce BNs and structure learning in them; then, a Particle Swarm Optimization (PSO)-based algorithm is proposed to solve the BN structure learning problem. In the proposed algorithm, which named BNC-PSO (Bayesian Network Construction algorithm using PSO), edge inserting/deleting is employed to make the particles have the ability to achieve the optimal solution, while a cycle removing procedure is used to prevent the generation of invalid solutions. The theorem of Markov chain is also used to prove the global convergence of the proposed algorithm. Finally, some experiments are designed to evaluate the performance of the proposed PSO-based algorithm. Experimental results indicate that BNC-PSO is worthy of being studied in the field of BNs construction. Meanwhile, it can significantly increase nearly 15% in the scoring metric values, comparing with other optimization-based algorithms.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.…
Seller Inventory # 9783330086913
- Title
- Bayesian Network Structure Learning
- Author
- Soulmaz Gheisari
- Publisher
- LAP LAMBERT Academic Publishing Jun 2018
- Publication year
- 2018
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3330086912
- ISBN 13
- 9783330086913
- Item weight
- 113 grams
- Dimensions
- 220x150x4 mm
"Synopsis" may belong to another edition of this title.
buchversandmimpf2000
Emtmannsberg, BAYE, Germany
AbeBooks seller since January 23, 2017
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