Advances in Supervised and Unsupervised Learning of Bayesian Networks: Application to Population Genetics - Brossura
Sinossi
Supervised classification and data clustering are two fundamental disciplines of data mining and machine learning where probabilistic graphical models, and particularly Bayesian networks, have become very popular paradigms. This book aims to contribute to the state of the art of both supervised classification and data clustering disciplines by providing new algorithms to learn Bayesian networks. On the one hand, the contributions related to supervised classification are focused on the discriminative learning of Bayesian network classifiers. Part of this book tries to motivate the use of this discriminative approach and presents new proposals to learn both structure and parameters of Bayesian network classifiers from a discriminative point of view. On the other hand, the part related to data clustering introduces new methods to deal with Bayesian model averaging for clustering. Additionally, the proposed methods are evaluated in diferent sinthetic and real datasets including a real problem taken from the field of population genetics.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
L'autore
Guzman Santafe received the M.Sc. and the PhD degrees in computer science from the University of the Basque Country in 2002 and 2007 respectively. He has worked as a machine learning expert in a computer security company for more than two years and currently he is a researcher at the Intelligent Systems Group (University of the Basque Country).
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Advances in Supervised and Unsupervised Learning of Bayesian Networks
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LAP LAMBERT Academic Publishing Aug 2010, 2010
ISBN 10: 3838333446
ISBN 13: 9783838333441
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Supervised classification and data clustering are two fundamental disciplines of data mining and machine learning where probabilistic graphical models, and particularly Bayesian networks, have become very popular paradigms. This book aims to contribute to the state of the art of both supervised classification and data clustering disciplines by providing new algorithms to learn Bayesian networks. On the one hand, the contributions related to supervised classification are focused on the discriminative learning of Bayesian network classifiers. Part of this book tries to motivate the use of this discriminative approach and presents new proposals to learn both structure and parameters of Bayesian network classifiers from a discriminative point of view. On the other hand, the part related to data clustering introduces new methods to deal with Bayesian model averaging for clustering. Additionally, the proposed methods are evaluated in diferent sinthetic and real datasets including a real problem taken from the field of population genetics. 224 pp. Englisch. Codice articolo 9783838333441
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Advances in Supervised and Unsupervised Learning of Bayesian Networks
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LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838333446
ISBN 13: 9783838333441
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Advances in Supervised and Unsupervised Learning of Bayesian Networks
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Advances in Supervised and Unsupervised Learning of Bayesian Networks
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
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Advances in Supervised and Unsupervised Learning of Bayesian Networks
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
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ISBN 13: 9783838333441
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Advances in Supervised and Unsupervised Learning of Bayesian Networks | Application to Population Genetics
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ISBN 13: 9783838333441
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Taschenbuch. Condizione: Neu. Advances in Supervised and Unsupervised Learning of Bayesian Networks | Application to Population Genetics | Guzmán Santafé | Taschenbuch | 224 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838333441 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Codice articolo 107438978
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Advances in Supervised and Unsupervised Learning of Bayesian Networks
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LAP LAMBERT Academic Publishing Aug 2010, 2010
ISBN 10: 3838333446
ISBN 13: 9783838333441
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Supervised classification and data clustering are two fundamental disciplines of data mining and machine learning where probabilistic graphical models, and particularly Bayesian networks, have become very popular paradigms. This book aims to contribute to the state of the art of both supervised classification and data clustering disciplines by providing new algorithms to learn Bayesian networks. On the one hand, the contributions related to supervised classification are focused on the discriminative learning of Bayesian network classifiers. Part of this book tries to motivate the use of this discriminative approach and presents new proposals to learn both structure and parameters of Bayesian network classifiers from a discriminative point of view. On the other hand, the part related to data clustering introduces new methods to deal with Bayesian model averaging for clustering. Additionally, the proposed methods are evaluated in diferent sinthetic and real datasets including a real problem taken from the field of population genetics.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 224 pp. Englisch. Codice articolo 9783838333441
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Advances in Supervised and Unsupervised Learning of Bayesian Networks : Application to Population Genetics
Editore:
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838333446
ISBN 13: 9783838333441
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Supervised classification and data clustering are two fundamental disciplines of data mining and machine learning where probabilistic graphical models, and particularly Bayesian networks, have become very popular paradigms. This book aims to contribute to the state of the art of both supervised classification and data clustering disciplines by providing new algorithms to learn Bayesian networks. On the one hand, the contributions related to supervised classification are focused on the discriminative learning of Bayesian network classifiers. Part of this book tries to motivate the use of this discriminative approach and presents new proposals to learn both structure and parameters of Bayesian network classifiers from a discriminative point of view. On the other hand, the part related to data clustering introduces new methods to deal with Bayesian model averaging for clustering. Additionally, the proposed methods are evaluated in diferent sinthetic and real datasets including a real problem taken from the field of population genetics. Codice articolo 9783838333441
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