Least squares recursive projection twin support vector machine for classification
Least squares recursive projection twin support vector machine for classification
复制标题
用于分类的最小二乘递归投影双支持向量机
DOI：
10.1016/j.patcog.2011.11.028
复制
发表时间：
2012-06-01
影响因子：
8
通讯作者：
Yang, Zhi-Min
中科院分区：
文献类型：
--
作者：
Shao, Yuan-Hai;Deng, Nai-Yang;Yang, Zhi-Min
在本文中，我们为二分类问题构建了最近提出的投影孪生支持向量机（PTSVM）的最小二乘版本。这种构建方式产生了一种极其简单且快速的算法，称为最小二乘投影孪生支持向量机（LSPTSVM），用于生成二分类器。与PTSVM不同的是，我们添加了一个正则化项，确保我们的LSPTSVM中的优化问题是正定的，从而具有更好的泛化能力。我们不是像通常那样求解两个对偶问题，而是通过求解两个线性方程组来解决两个修正的原始问题，而PTSVM需要求解两个二次规划问题以及两个线性方程组。我们在公开可用数据集上的实验表明，我们的LSPTSVM具有与PTSVM相当的分类精度，但计算时间显著减少。（C）2011爱思唯尔有限公司。保留所有权利。
In this paper we formulate a least squares version of the recently proposed projection twin support vector machine (PTSVM) for binary classification. This formulation leads to extremely simple and fast algorithm, called least squares projection twin support vector machine (LSPTSVM) for generating binary classifiers. Different from PTSVM, we add a regularization term, ensuring the optimization problems in our LSPTSVM are positive definite and resulting better generalization ability. Instead of usually solving two dual problems, we solve two modified primal problems by solving two systems of linear equations whereas PTSVM need to solve two quadratic programming problems along with two systems of linear equations. Our experiments on publicly available datasets indicate that our LSPTSVM has comparable classification accuracy to that of PTSVM but with remarkably less computational time. (C) 2011 Elsevier Ltd. All rights reserved.