Learning to Classify Text Using Support Vector Machines [electronic resource] /

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.

Bibliographic Details

Main Authors:	Joachims, Thorsten. author., SpringerLink (Online service)
Format:	Texto biblioteca
Language:	eng
Published:	Boston, MA : Springer US : Imprint: Springer, 2002
Subjects:	Computer science., Data structures (Computer science)., Information storage and retrieval., Artificial intelligence., Computer Science., Artificial Intelligence (incl. Robotics)., Information Storage and Retrieval., Data Structures, Cryptology and Information Theory., Information Systems Applications (incl. Internet).,
Online Access:	http://dx.doi.org/10.1007/978-1-4615-0907-3
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