Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
407581 | Neurocomputing | 2013 | 9 Pages |
Abstract
This paper introduces Sphere Support Vector Machines (SVMs) as the new fast classification algorithm based on combining a minimal enclosing ball approach, state of the art nearest point problem solvers and probabilistic techniques. The blending of the three significantly speeds up the training phase of SVMs and also attains practically the same accuracy as the other classification models over several large real datasets within the strict validation frame of a double (nested) cross-validation. The results shown are promoting SphereSVM as outstanding alternatives for handling large and ultra-large datasets in a reasonable time without switching to various parallelization schemes for SVM algorithms recently proposed.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Robert Strack, Vojislav Kecman, Beata Strack, Qi Li,