Seller: Ria Christie Collections, Uxbridge, United Kingdom
£ 117.36
Quantity: Over 20 available
Add to basketCondition: New. In.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
£ 117.36
Quantity: Over 20 available
Add to basketCondition: New. In.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
£ 137.84
Quantity: Over 20 available
Add to basketCondition: New. In.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
£ 137.84
Quantity: Over 20 available
Add to basketCondition: New. In.
Condition: Sehr gut. Zustand: Sehr gut | Seiten: 372 | Sprache: Englisch | Produktart: Bücher | 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.
Condition: New. pp. 343.
Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
£ 197.23
Quantity: Over 20 available
Add to basketCondition: New.
paperback. Condition: New. New. book.
Seller: Mispah books, Redhill, SURRE, United Kingdom
Hardcover. Condition: Like New. Like New. book.
Condition: New.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
£ 209.52
Quantity: Over 20 available
Add to basketCondition: New. In.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
£ 209.52
Quantity: Over 20 available
Add to basketCondition: New. In.
Condition: As New. Unread book in perfect condition.
Taschenbuch. Condition: Neu. Meta-Learning in Computational Intelligence | Norbert Jankowski (u. a.) | Taschenbuch | Studies in Computational Intelligence | ix | Englisch | 2013 | Springer | EAN 9783642268588 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Seller: Mispah books, Redhill, SURRE, United Kingdom
Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
£ 231.41
Quantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
Condition: New. pp. 372.
Language: English
Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2013
ISBN 10: 3642268587 ISBN 13: 9783642268588
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: 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.
Language: English
Published by Springer Berlin Heidelberg, 2011
ISBN 10: 3642209793 ISBN 13: 9783642209796
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: 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.
Seller: Revaluation Books, Exeter, United Kingdom
Paperback. Condition: Brand New. 2011 edition. 372 pages. 9.25x6.10x0.88 inches. In Stock.
Language: English
Published by Springer-Verlag New York Inc, 2011
ISBN 10: 3642209793 ISBN 13: 9783642209796
Seller: Revaluation Books, Exeter, United Kingdom
Hardcover. Condition: Brand New. 372 pages. 9.00x6.00x1.00 inches. In Stock.
Seller: Brook Bookstore On Demand, Napoli, NA, Italy
Condition: new. Questo è un articolo print on demand.
Seller: Brook Bookstore On Demand, Napoli, NA, Italy
Condition: new. Questo è un articolo print on demand.
Condition: New. Print on Demand.
Condition: New. Print on Demand pp. 343.
Condition: New. Print on Demand.
Condition: New. PRINT ON DEMAND.
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. PRINT ON DEMAND pp. 343.