Recursive training methods for robust classification: a sequential analytic centering approach to the support vector machine

Katherine Comanor; Lieven Vandenberghe

doi:10.1117/12.507878

24 December 2003 Recursive training methods for robust classification: a sequential analytic centering approach to the support vector machine

Katherine Comanor, Lieven Vandenberghe

Author Affiliations +

Proceedings Volume 5205, Advanced Signal Processing Algorithms, Architectures, and Implementations XIII; (2003) https://doi.org/10.1117/12.507878
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

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.

Citation Download Citation

Katherine Comanor and Lieven Vandenberghe "Recursive training methods for robust classification: a sequential analytic centering approach to the support vector machine", Proc. SPIE 5205, Advanced Signal Processing Algorithms, Architectures, and Implementations XIII, (24 December 2003); https://doi.org/10.1117/12.507878

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available