Free Learning to Classify Text Using Support Vector Machines

Author: Thorsten Joachims
Edition: 2002
Binding: Hardcover
ISBN: 079237679X
Publisher: Springer
Features:

Learning to Classify Text Using Support Vector Machines (The Springer International Series in Engineering and Computer Science)

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. Search and download computer ebooks Learning to Classify Text Using Support Vector Machines (The Springer International Series in Engineering and Computer Science) for free.
Learning to Classify Text Using Support Vector Machines SPRNC 9781461352983 09781461352983. Download Learning to Classify Text Using Support Vector Machines computer ebooks
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

Learning To Classify Text Using Support Vector Machines (the Springer Internatio

author thorsten joachims format paperback language english publication year 30 04 2013 series the springer international series in engineering and computer science subject engineering technology subject 2 technology general reference title learning to classify text using support vector machines the springer international series in engineering and computer science author thorsten joachims publisher springer publication date nov 01 2012 pages 224 binding paperback edition reprint dimensions 6 10 w

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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 detail

"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 detail

Learning to Classify Text Using Support Vector Machines Free

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