Synopsis
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.
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Bayesian Network Structure Learning
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LAP LAMBERT Academic Publishing Jun 2018, 2018
ISBN 10: 3330086912
ISBN 13: 9783330086913
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 64 pp. Englisch. Seller Inventory # 9783330086913
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gheisari SoulmazEducation: Science and Research University,Tehran, Iran. Faculty member, professor assistant of computer engineering in Islamic Azad University Pardis branch, Tehran, Iran. Also worked in Electricity Distribution Comp. Seller Inventory # 385708075
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Bayesian Network Structure Learning
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LAP LAMBERT Academic Publishing Jun 2018, 2018
ISBN 10: 3330086912
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Taschenbuch. Condition: Neu. 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
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Bayesian Network Structure Learning
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9783330086913
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Taschenbuch. Condition: Neu. Bayesian Network Structure Learning | Soulmaz Gheisari | Taschenbuch | 64 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9783330086913 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 114050975
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