基于NSVM的核空间训练数据减少方法

针对核空间中大数据集的计算代价高问题, 提出用NSVM方法减少分类器的训练数据. 先用NSVM、核主成分分析(KPCA)和贪婪KPCA分别从全部训练数据中提取训练分类器的子集; 再用子集训练分类器, 并用训练和测试数据的错分率对分类结果进行评价. 在两个数据集和两种分类器中, 用KPCA提取的子集训练的分类器的分类性能弱于NSVM和贪婪KPCA, 但用贪婪KPCA提取的子集训练的分类器的泛化能力弱于NSVM. 仿真结果表明, 用NSVM方法提取的子集训练的分类器, 不仅保证了分类器的泛化能力, 也降低了分类算法的计算复杂度.

Nonlinear Support Vector Machine for Training Data Reduction in Kernel Space

Aiming at the high computational cost issue for large data sets in kernel space, the non-linear support vector machine (NSVM) is proposed to reduce training data of classifier. First, a subset of training classifier is extracted from full training data by using NSVM, kernel principal component analysis (KPCA), and greedy kernel principal component analysis (GKPCA), respectively. Then, the classifier is trained by those subsets, respectively. Finally, the classification results are evaluated by the error rate of the training and test data. The classification performance of the classifier trained by the subsets from the KPCA method is inferior to those of from the NSVM and the GKPCA methods, but the generalization of the classifier trained by the subset from the GKPCA method is inferior to those of from the NSVM method for two data sets through two the classifiers. Simulation results indicate that the classifier trained by the subset from the NSVM method not only ensures the generalization ability of classifier, but also reduces the computational complexity of the classification algorithm.