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.

222 pages, Hardcover

First published April 30, 2002

Reviews

[Nick Black]

I've got to build up a foundation in machine learning; I know absolutely nothing about the field and it's beginning to creep into my areas of expertise. In a VoIPSecurity working group meeting the other day, a "C4.5" tree came up and I almost crapped myself -- wtf, is that a decision-tree-thing? a data-structure-thing? a some-other-damn-thing? OH NOOOO PEOPLE IN THE ROOM KNOW THINGS I DON'T AND THEY'RE ALL LOOKING AT ME WHY AM I NOT MORE INTELLIGENT AUGH. It turned out that yes, it was just a decision-tree-thing (enjoy the paper here, along with slides, if you for some reason care), and that no one else knew the details either...but I didn't make it this far by only knowing as much as anyone else in the room =D.