A fast learning algorithm for One-Class Slab Support Vector Machines

One Class Slab Support Vector Machines (OCSSVM) have turned out to be better in terms of accuracy in certain classes of classification problems than the traditional SVMs and One Class SVMs or even other One class classifiers. This paper proposes fast training method for One Class Slab SVMs using an...

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Bibliographic Details
Published inarXiv.org
Main Authors Kumar, Bagesh, Sinha, Ayush, Chakrabarti, Sourin, Vyas, O P
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 03.05.2021
Subjects
Computer Science - Learning
Optimization
Quadratic programming
Solvers
Statistics - Machine Learning
Support vector machines
Training
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Summary:One Class Slab Support Vector Machines (OCSSVM) have turned out to be better in terms of accuracy in certain classes of classification problems than the traditional SVMs and One Class SVMs or even other One class classifiers. This paper proposes fast training method for One Class Slab SVMs using an updated Sequential Minimal Optimization (SMO) which divides the multi variable optimization problem to smaller sub problems of size two that can then be solved analytically. The results indicate that this training method scales better to large sets of training data than other Quadratic Programming (QP) solvers.
ISSN:2331-8422
DOI:10.48550/arxiv.2011.03243