A sequential minimal optimization algorithm for the all-distances support vector machine

Candel D.; Ñanculef R.; Concha, C; Allende, H.

Keywords: optimization, support, classification, machines, training, algorithms, computer, data, vision, kernel, machine, vector, benchmark, Sequential, minimal, Objective, Gears, Multi, Multi-category

Abstract

The All-Distances SVM is a single-objective light extension of the binary ?-SVM for multi-category classification that is competitive against multi-objective SVMs, such as One-against-the-Rest SVMs and One-against-One SVMs. Although the model takes into account considerably less constraints than previous formulations, it lacks of an efficient training algorithm, making its use with medium and large problems impracticable. In this paper, a Sequential Minimal Optimization-like algorithm is proposed to train the All-Distances SVM, making large problems abordable. Experimental results with public benchmark data are presented to show the performance of the AD-SVM trained with this algorithm against other single-objective multi-category SVMs. © 2010 Springer-Verlag.

Más información

Título de la Revista: BIO-INSPIRED SYSTEMS AND APPLICATIONS: FROM ROBOTICS TO AMBIENT INTELLIGENCE, PT II
Volumen: 6419
Editorial: SPRINGER INTERNATIONAL PUBLISHING AG
Fecha de publicación: 2010
Página de inicio: 484
Página final: 491
URL: http://www.scopus.com/inward/record.url?eid=2-s2.0-78649954067&partnerID=q2rCbXpz