Jankowski Norbert (26 results)

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Condition: Sehr gut. Zustand: Sehr gut | Seiten: 372 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

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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.

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.

Buch. Condition: 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.

Paperback. Condition: Brand New. 2011 edition. 372 pages. 9.25x6.10x0.88 inches. In Stock.

Hardcover. Condition: Brand New. 372 pages. 9.00x6.00x1.00 inches. In Stock.

Paperback. Condition: Brand New. 2008 edition. 261 pages. German language. 8.27x5.75x0.55 inches. In Stock.

Condition: Gut. 4°. 240, 240 Seiten. Orig.-Leinwand im OU. Seltene Privatdokumentation, nicht im KVK. - Reich illustrierte Reise einer 11-köpfigen Gruppenreise (Personen aus Kunst, Kultur und Architektur) nach Persien. Jeder der Mitreisenden erzählt von den Erlebnissen eines bestimmten Tages mit entsprechendem Bildmaterial. Die Mitglieder der Reise waren Norbert Bisky, M. Esser, St. Koal, A. Wätjen, A. Cornelsen, S. Kluckow, A. Schmitz, P. Kahlfeldt, T. Rantur, C. Baumhöver und M. Adli. - Aus der aufgelösten Architektursammlung P. u. P. Kahlfeldt Berlin. Gä-II-1-53 Bei Bestellungen auf Rechnung bleibt Vorkasse vorbehalten. Bestellungen erfolgen ohne Gewähr und gelten erst nach Bestätigung der Verfügbarkeit. Wir bieten günstigere Versandvariante für Bücher bis 1000 gr. Bitte Anfragen. Payment in advance is reserved for orders on account. Orders are made without guarantee and are only valid after availability has been confirmed. The shipping costs to non-EU countries may vary depending on the weight. We also offer cheaper shipping options for books up to 1000g. Sprache: Deutsch Gewicht in Gramm: 2000.

Condition: Gut. Goldprägung auf vorderem Einbanddeckel und Rücken, Illustrationen von Werner Klemke, 365 Seiten Schutzumschlag minimal bestossen, minimal beschmutzt und minimal gebräunt, Vor- und Nachsatz leicht gebräunt Sprache: Deutsch Gewicht in Gramm: 495 13x20cm Leineneinband mit Schutzumschlag.

Taschenbuch. 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.

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.

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.

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.

Taschenbuch. Condition: Neu. Meta-Learning in Computational Intelligence | Norbert Jankowski (u. a.) | Taschenbuch | 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 Print on Demand.

Condition: New. Print on Demand pp. 372 1237 Illus. (76 Col.).

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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.

Condition: New. PRINT ON DEMAND pp. 372.