9783843353120 - consensus policies to solve bioinformatic problems: through bayesian network classifiers and estimation of distribution algorithms di armañanzas, rubén (6 risultati)
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Da: moluna, Greven, Germaniamoluna
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Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: Neu. Consensus Policies to Solve Bioinformatic Problems | Through Bayesian Network Classifiers and Estimation of Distribution Algorithms | Rubén Armañanzas | Taschenbuch | 388 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783843353120 | Verantwortliche Person für die EU: preigu GmbH & Co.… KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books
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paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Disciplines of bioinformatics and computational biology have emerged from the convergence of the new omics fields and the computational tools that are needed to manage, store and analyze the huge amount of data produced by them. On…e of the most classical problems in computational biology research is to extract knowledge from population studies. This kind of research is mapped by the machine learning discipline into the supervised classification problems. Several models exist to accomplish this task, but, in order to extract useful biological knowledge, the classifiers based on Bayesian networks are of the most useful. In optimization, classical search strategies are unfeasible to deal with high-dimensionality biological problems, where the current computer power is still insufficient to provide exhaustive searches. Therefore, machine learning and optimization procedures need accommodation to the specificities of the novel biological data. This book aims to contribute to the state-of-the-art of machine learning techniques adapted for dealing with computational biology problems. 388 pp. Englisch.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Disciplines of bioinformatics and computational biology have emerged from the convergence of the new omics fields and the computational tools that are needed to manage, store and analyze the huge amount of data produced by them. One of… the most classical problems in computational biology research is to extract knowledge from population studies. This kind of research is mapped by the machine learning discipline into the supervised classification problems. Several models exist to accomplish this task, but, in order to extract useful biological knowledge, the classifiers based on Bayesian networks are of the most useful. In optimization, classical search strategies are unfeasible to deal with high-dimensionality biological problems, where the current computer power is still insufficient to provide exhaustive searches. Therefore, machine learning and optimization procedures need accommodation to the specificities of the novel biological data. This book aims to contribute to the state-of-the-art of machine learning techniques adapted for dealing with computational biology problems.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 388 pp. Englisch.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Disciplines of bioinformatics and computational biology have emerged from the convergence of the new omics fields and the computational tools that are needed to manage, store and analyze the huge amount of data produced by them. One of…the most classical problems in computational biology research is to extract knowledge from population studies. This kind of research is mapped by the machine learning discipline into the supervised classification problems. Several models exist to accomplish this task, but, in order to extract useful biological knowledge, the classifiers based on Bayesian networks are of the most useful. In optimization, classical search strategies are unfeasible to deal with high-dimensionality biological problems, where the current computer power is still insufficient to provide exhaustive searches. Therefore, machine learning and optimization procedures need accommodation to the specificities of the novel biological data. This book aims to contribute to the state-of-the-art of machine learning techniques adapted for dealing with computational biology problems.
