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Learning to Classify Text Using Support Vector Machines: 668 (The Springer International Series in Engineering and Computer Science) - Hardcover
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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.
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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.
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- VerlagSpringer
- Erscheinungsdatum2002
- ISBN 10 079237679X
- ISBN 13 9780792376798
- EinbandTapa dura
- SpracheEnglisch
- Anzahl der Seiten228
- Kontakt zum HerstellerNicht verfügbar
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Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms
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Hardcover. Zustand: 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. Bestandsnummer des Verkäufers G-122-170
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Learning to Classify Text Using Support Vector Machines
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Hardcover. Zustand: Good. No Jacket. Former library book; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 1.05. Bestandsnummer des Verkäufers G079237679XI3N10
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Learning to Classify Text Using Support Vector Machines : Methods, Theory and Algorithms
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Learning to Classify Text Using Support Vector Machines. Methods, Theory and Algorithms
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Kluwer Academic Publishers, 2001
ISBN 10: 079237679X
ISBN 13: 9780792376798
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Learning to Classify Text Using Support Vector Machines (The Springer International Series in Engineering and Computer Science)
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Learning to Classify Text Using Support Vector Machines
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Gebunden. Zustand: 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 . Bestandsnummer des Verkäufers 5970343
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Learning to Classify Text Using Support Vector Machines (The Springer International Series in Engineering and Computer Science, 668)
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Learning to Classify Text Using Support Vector Machines
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Springer US, Springer US Apr 2002, 2002
ISBN 10: 079237679X
ISBN 13: 9780792376798
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Buch. Zustand: 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. Bestandsnummer des Verkäufers 9780792376798
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Learning to Classify Text Using Support Vector Machines
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Buch. Zustand: 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. Bestandsnummer des Verkäufers 9780792376798
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Learning to Classify Text Using Support Vector Machines
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Buch. Zustand: 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. Bestandsnummer des Verkäufers 9780792376798
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