Optimization issues in training support vector machines
(invited lecture for ALT 2005)

Author: Chih-Jen Lin

Affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

Abstract. Optimization plays an important role in many machine learning methods. In this talk we will introduce and discuss how optimization techniques are applied to support vector machines (SVM), a promising classification method. Training SVM involves solving a huge quadratic programming problem (QP). There are many algorithmic issues related to this special type of QP. We will particularly focus on the decomposition methods for solving large SVM dual optimization problems. A brief introduction on the practical use of SVM may also be included.

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