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

TL;DR

This paper introduces a fast training algorithm for One Class Slab SVMs using an updated SMO approach, improving scalability and efficiency over traditional QP solvers for large datasets.

Contribution

It presents a novel SMO-based training method for One Class Slab SVMs that enhances scalability and reduces training time.

Findings

Training scales better to large datasets

Outperforms traditional QP solvers in speed

Maintains high accuracy in classification

Abstract

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.