基于中心加权的局部核向量机算法

为了解决大规模非线性分类中局部学习的不平衡性问题,提出一种改进的局部支持向量机算法,在高维特征空间中聚类后,为每一个簇构造局部非线性支持向量机。为了克服簇内样本的分布不均衡问题,根据闭合超平面不规则边界的几何特点,经过梯度下降寻找稳定均衡向量,以此构造簇几何中心;再结合簇密度中心共同约束类心形成双重加权中心。然后通过求解加权最小闭球问题实现对大规模样本向量的分类。对照实验显示,除了个别数据集以外,改进的算法在训练时间、测试时间以及测试精度等方面都比另外两种分类算法表现更佳。

Center-Weighted and Localized Core Vector Machine Algorithm

An improved algorithm for localized support vector machine is proposed to resolve the imbalance of local learning problem in nonlinear classifications on large data sets. The algorithm uses the supervised clustering algorithm for clustering in a feature space of high dimension and then constructs local nonlinear support vector machines for each cluster. According to the geometric feature of irregular borders of enclosing sphere, the geometric center for a stable equilibrium point is constructed and a dual-weighted center of two relevant weights is formed through calculating density center of the cluster. At last, the classification of large data set is carried out by solving the problem of weighted minimum enclosing ball. Compared with the other two algorithms of controlled group, the proposed algorithm shows shorter training time and testing time as well as higher testing precision except for some individual data sets.