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Ajouter au panierEtat : Sehr gut. Zustand: Sehr gut | Seiten: 372 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
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Langue: anglais
Edité par Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2013
ISBN 10 : 3642268587 ISBN 13 : 9783642268588
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open. Modern data mining packages contain numerous modules for data acquisition, pre-processing, feature selection and construction, instance selection, classification, association and approximation methods, optimization techniques, pattern discovery, clusterization, visualization and post-processing. A large data mining package allows for billions of ways in which these modules can be combined. No human expert can claim to explore and understand all possibilities in the knowledge discovery process. This is where algorithms that learn how to learnl come to rescue. Operating in the space of all available data transformations and optimization techniques these algorithms use meta-knowledge about learning processes automatically extracted from experience of solving diverse problems. Inferences about transformations useful in different contexts help to construct learning algorithms that can uncover various aspects of knowledge hidden in the data. Meta-learning shifts the focus of the whole CI field from individual learning algorithms to the higher level of learning how to learn. This book defines and reveals new theoretical and practical trends in meta-learning, inspiring the readers to further research in this exciting field.
Langue: anglais
Edité par Springer Berlin Heidelberg, 2011
ISBN 10 : 3642209793 ISBN 13 : 9783642209796
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 235,39
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Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open. Modern data mining packages contain numerous modules for data acquisition, pre-processing, feature selection and construction, instance selection, classification, association and approximation methods, optimization techniques, pattern discovery, clusterization, visualization and post-processing. A large data mining package allows for billions of ways in which these modules can be combined. No human expert can claim to explore and understand all possibilities in the knowledge discovery process. This is where algorithms that learn how to learnl come to rescue. Operating in the space of all available data transformations and optimization techniques these algorithms use meta-knowledge about learning processes automatically extracted from experience of solving diverse problems. Inferences about transformations useful in different contexts help to construct learning algorithms that can uncover various aspects of knowledge hidden in the data. Meta-learning shifts the focus of the whole CI field from individual learning algorithms to the higher level of learning how to learn. This book defines and reveals new theoretical and practical trends in meta-learning, inspiring the readers to further research in this exciting field.
Langue: anglais
Edité par Springer Berlin Heidelberg, Springer Berlin Heidelberg Jun 2011, 2011
ISBN 10 : 3642209793 ISBN 13 : 9783642209796
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 235,39
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Ajouter au panierBuch. Etat : Neu. Neuware -Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open.Modern data mining packages contain numerous modules for data acquisition, pre-processing, feature selection and construction, instance selection, classification, association and approximation methods, optimization techniques, pattern discovery, clusterization, visualization and post-processing. A large data mining package allows for billions of ways in which these modules can be combined. No human expert can claim to explore and understand all possibilities in the knowledge discovery process.This is where algorithms that learn how to learnl come to rescue.Operating in the space of all available data transformations and optimization techniques these algorithms use meta-knowledge about learning processes automatically extracted from experience of solving diverse problems. Inferences about transformations useful in different contexts help to construct learning algorithms that can uncover various aspects of knowledge hidden in the data. Meta-learning shifts the focus of the whole CI field from individual learning algorithms to the higher level of learning how to learn.This book defines and reveals new theoretical and practical trends in meta-learning, inspiring the readers to further research in this exciting field.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 372 pp. Englisch.
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierPaperback. Etat : Brand New. 2011 edition. 372 pages. 9.25x6.10x0.88 inches. In Stock.
Langue: anglais
Edité par Springer-Verlag New York Inc, 2011
ISBN 10 : 3642209793 ISBN 13 : 9783642209796
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierHardcover. Etat : Brand New. 372 pages. 9.00x6.00x1.00 inches. In Stock.
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Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Ajouter au panierEtat : New. PRINT ON DEMAND.
Langue: anglais
Edité par Springer Berlin Heidelberg Aug 2013, 2013
ISBN 10 : 3642268587 ISBN 13 : 9783642268588
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 208,64
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open. Modern data mining packages contain numerous modules for data acquisition, pre-processing, feature selection and construction, instance selection, classification, association and approximation methods, optimization techniques, pattern discovery, clusterization, visualization and post-processing. A large data mining package allows for billions of ways in which these modules can be combined. No human expert can claim to explore and understand all possibilities in the knowledge discovery process. This is where algorithms that learn how to learnl come to rescue. Operating in the space of all available data transformations and optimization techniques these algorithms use meta-knowledge about learning processes automatically extracted from experience of solving diverse problems. Inferences about transformations useful in different contexts help to construct learning algorithms that can uncover various aspects of knowledge hidden in the data. Meta-learning shifts the focus of the whole CI field from individual learning algorithms to the higher level of learning how to learn. This book defines and reveals new theoretical and practical trends in meta-learning, inspiring the readers to further research in this exciting field. 372 pp. Englisch.