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.

翻译：与传统的SVM和1类SVM或甚至其他的1类分类者相比,一个级的Slab支持矢量机(OCSSVM)在某些分类问题的准确性方面比传统的SVM和1类SVM或甚至其他的1类分类者要好,本文件提出对1类Slab SVM的快速培训方法,使用更新的序列最小优化(SMO),将多变量优化问题分为小于2号尺寸的子问题,然后通过分析解决。结果显示,这种培训方法比其他夸大程序(QP)解答器(QP)的大型培训数据比例要好。