Seller: ThriftBooks-Dallas, Dallas, TX, U.S.A.
Hardcover. Condition: Good. No Jacket. Former library book; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 1.05.
Seller: Better World Books: West, Reno, NV, U.S.A.
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Seller: Ammareal, Morangis, France
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Add to basketHardcover. Condition: Très bon. Ancien livre de bibliothèque avec équipements. Edition 2002. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 2002. Ammareal gives back up to 15% of this item's net price to charity organizations.
Seller: Magus Books Seattle, Seattle, WA, U.S.A.
Hardcover. Condition: VG. used hardcover copy in illustrated boards, no jacket, as issued. light shelfwear, corners perhaps slightly bumped. pages and binding are clean, straight and tight. there are no marks to the text or other serious flaws.
Published by Kluwer Academic Publishers, 2001
ISBN 10: 079237679X ISBN 13: 9780792376798
Seller: Librería Ofisierra, Galapagar, M, Spain
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Add to basketHardcover. Good condition. Dog-eared corners. Libro.
Seller: Shakespeare Book House, Rockford, IL, U.S.A.
Condition: New. The item is Brand New!
Seller: BennettBooksLtd, North Las Vegas, NV, U.S.A.
Hardcover. Condition: New. In shrink wrap. Looks like an interesting title!
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
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Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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US$ 132.72
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications.Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.
Published by Springer US, Springer US, 2002
ISBN 10: 079237679X ISBN 13: 9780792376798
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
US$ 134.60
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Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications.Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.
Published by Springer US, Springer US Apr 2002, 2002
ISBN 10: 079237679X ISBN 13: 9780792376798
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
US$ 125.92
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Add to basketBuch. Condition: Neu. Neuware -Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications.Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 224 pp. Englisch.
Seller: Mispah books, Redhill, SURRE, United Kingdom
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Add to basketHardcover. Condition: Like New. Like New. book.
Published by Springer US Apr 2002, 2002
ISBN 10: 079237679X ISBN 13: 9780792376798
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
US$ 125.92
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Add to basketBuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications.Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning. 224 pp. Englisch.
Seller: moluna, Greven, Germany
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with .
Seller: moluna, Greven, Germany
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Add to basketGebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with .
Published by Springer-Verlag New York Inc., 2012
ISBN 10: 1461352983 ISBN 13: 9781461352983
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 385.
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Add to basketHardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 544.
Published by Springer US, Springer New York Nov 2012, 2012
ISBN 10: 1461352983 ISBN 13: 9781461352983
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
US$ 125.92
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications.Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 228 pp. Englisch.
Published by Springer US Nov 2012, 2012
ISBN 10: 1461352983 ISBN 13: 9781461352983
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
US$ 245.52
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications.Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning. 228 pp. Englisch.