论文标题
一级平板支持向量机的快速学习算法
A fast learning algorithm for One-Class Slab Support Vector Machines
论文作者
论文摘要
与传统的SVM，一个类SVM，甚至其他一个类别分类器相比，在某些类别的分类问题中，在某些类别的分类问题的准确性方面，一个类平板支持矢量机（OCSSVM）的准确性已经更好。本文建议使用更新的顺序最小优化（SMO）为一个类平板SVM的快速训练方法，该方法将多变量优化问题划分为尺寸二大的较小子问题，然后可以通过分析解决。结果表明，与其他二次编程（QP）求解器相比，这种训练方法比大量训练数据缩放得更好。
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.
