Learning to Classify Text Using Support Vector Machines by Thorsten Joachims

Learning to Classify Text Using Support Vector Machines by Thorsten Joachims

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.

SKU	Unavailable
ISBN 13	9780792376798
ISBN 10	079237679X
Title	Learning to Classify Text Using Support Vector Machines
Author	Thorsten Joachims
Series	The Springer International Series In Engineering And Computer Science
Condition	Unavailable
Binding Type	Hardback
Publisher	Kluwer Academic Publishers
Year published	2002-04-30
Number of pages	205
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
Note	Unavailable