Meta-Learning in Computational Intelligence: 358 (Studies in Computational Intelligence, 358) - Hardcover
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.
"synopsis" may belong to another edition of this title.
- PublisherSpringer
- Publication date2011
- ISBN 10 3642209793
- ISBN 13 9783642209796
- BindingHardcover
- Number of pages368
- EditorJankowski Norbert, Duch Włodzisław, Grąbczewski Krzysztof
Buy New
Learn more about this copy
£ 199.55
Shipping:
FREE
Within U.S.A.
Top Search Results from the AbeBooks Marketplace
Seller Image
Meta-Learning in Computational Intelligence (Studies in Computational Intelligence) [Hardcover ]
Seller:
Rating
Book Description Hardcover. Condition: new. Seller Inventory # 9783642209796
Buy New
£ 199.55
Convert currency
Seller Image
Meta-Learning in Computational Intelligence
Published by
Springer Berlin Heidelberg
(2011)
ISBN 10: 3642209793
ISBN 13: 9783642209796
New
Hardcover
Quantity: > 20
Seller:
Rating
Book Description Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recent research in Meta-learning in computational intelligence Presents new Developments and Trends in Computational Intelligence and Learning Written by leading experts in the fieldComputational Intelligence (CI) community has de. Seller Inventory # 5052397
Buy New
£ 174.81
Convert currency
Stock Image
Meta-Learning in Computational Intelligence
Published by
Springer
(2011)
ISBN 10: 3642209793
ISBN 13: 9783642209796
New
Hardcover
Quantity: > 20
Seller:
Rating
Book Description Condition: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. Seller Inventory # ria9783642209796_lsuk
Buy New
£ 208.92
Convert currency
Stock Image
Meta-Learning in Computational Intelligence (Studies in Computational Intelligence, 358)
Published by
Springer
(2011)
ISBN 10: 3642209793
ISBN 13: 9783642209796
New
Hardcover
Quantity: > 20
Seller:
Rating
Book Description Condition: New. Seller Inventory # ABLIING23Mar3113020220704
Buy New
£ 216.16
Convert currency
Seller Image
Meta-Learning in Computational Intelligence
Published by
Springer Berlin Heidelberg Jun 2011
(2011)
ISBN 10: 3642209793
ISBN 13: 9783642209796
New
Hardcover
Quantity: 2
Seller:
Rating
Book Description Buch. Condition: 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. Seller Inventory # 9783642209796
Buy New
£ 208.22
Convert currency
Seller Image
Meta-Learning in Computational Intelligence
Published by
Springer Berlin Heidelberg
(2011)
ISBN 10: 3642209793
ISBN 13: 9783642209796
New
Hardcover
Quantity: 1
Seller:
Rating
Book Description 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 Inventory # 9783642209796
Buy New
£ 210.71
Convert currency