Share Email Print

Proceedings Paper

Recursive training methods for robust classification: a sequential analytic centering approach to the support vector machine
Author(s): Katherine Comanor; Lieven Vandenberghe
Format Member Price Non-Member Price
PDF $17.00 $21.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The support vector machine (SVM) is a supervised learning algorithm used in a variety of applications, including robust target classification. The SVM training problem can be formulated as dense quadratic programming problem (QP). In practice, this QP is solved in batch mode, using general-purpose interior-point solvers. Although quite efficient, these implementations are not well suited in situations where the training vectors are made available sequentially. In this paper we discuss a recursive algorithm for SVM training. The algorithm is based on efficient updates of approximate solutions on the dual central path of the QP and can be analyzed using the convergence theory recently developed for interior-point methods. The idea is related to cutting-plane methods for large-scale optimization and sequential analytic centering techniques used successfully in set-membership estimation methods in signal processing.

Paper Details

Date Published: 24 December 2003
PDF: 10 pages
Proc. SPIE 5205, Advanced Signal Processing Algorithms, Architectures, and Implementations XIII, (24 December 2003); doi: 10.1117/12.507878
Show Author Affiliations
Katherine Comanor, Univ. of California/Los Angeles (United States)
Lieven Vandenberghe, Univ. of California/Los Angeles (United States)

Published in SPIE Proceedings Vol. 5205:
Advanced Signal Processing Algorithms, Architectures, and Implementations XIII
Franklin T. Luk, Editor(s)

© SPIE. Terms of Use
Back to Top