2002 | OriginalPaper | Chapter
Training Inductive Support Vector Machines
Author : Thorsten Joachims
Published in: Learning to Classify Text Using Support Vector Machines
Publisher: Springer US
Included in: Professional Book Archive
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood in principle, there are many issues to be considered in designing an SVM learner. In particular, for large learning tasks with many training examples, off-the-shelf optimization techniques for general quadratic programs such as Newton, Quasi Newton, etc., quickly become intractable in their memory and time requirements.