A Fast Least Squares Support Vector Machine Training Approach
A Fast Least Squares Support Vector Machine Training Approach
复制标题
DOI：
10.1109/icicisys.2010.5658435
复制
发表时间：
2010-12
期刊：
影响因子：
--
通讯作者：
Jing Cui;Ning Ye;Qiaolin Ye;Jie Hu
中科院分区：
文献类型：
--
作者：
Jing Cui;Ning Ye;Qiaolin Ye;Jie Hu
提出了一种用于分类问题的快速最小二乘支持向量机训练方法。FTLSVM的分类平面是通过求解一个线性方程组来生成的，而不是像支持向量机那样通过求解一个二次规划问题来生成的，因为支持向量机不适合解决大规模的分类问题。一些简单的技术被用来解决线性系统，以获得快速的计算时间。邻近支持向量机（PSVM）最大化方向w和阈值B以获得更快的计算时间。在本文中，我们的方法最大限度地提高了两个边界平面之间的相对于方向w的利润。我们的方法是基于最小二乘支持向量机，它给出的结果是相媲美的SVM在使用中，在测试集的正确性，但具有相当快的计算时间。最后，该方法与其他方法使用合成和UCI数据集进行了比较。
A Fast Least Squares Support Vector Machine Training Approach (FTLSVM) to classification problem is proposed in this paper. The classification plane of FTLSVM is generated by solving a linear system of equations instead of a quadratic programming problem as for SVMs that is not fit for solving large-scale classification problems. Some simple techniques are used to solve the linear system to obtain fast computational time. The proximal support vector machines (PSVM) maximizes both direction w and threshold b to obtain faster computational time. In the paper our approach maximizes the margin between the two bounding planes with respect to the direction w. Our approach is based on LS-SVM, which gives results that are comparable to SVMs in use, in terms of test set correctness, but with considerably faster computational time. Lastly, the approach is compared with other approaches using synthetic and UCI datasets.