一种改进的支持向量机NN-SVM

支持向量机(SVM)是一种较新的机器学习方法，它利用靠近边界的少数向量构造一个最优分类超平面。在训练分类器时，SVM的着眼点在于两类的交界部分，那些混杂在另一类中的点往往无助于提高分类器的性能，反而会大大增加训练器的计算负担，同时它们的存在还可能造成过学习，使泛化能力减弱．为了改善支持向量机的泛化能力，该文在其基础上提出了一种改进的SVM—NN-SVM：它先对训练集进行修剪，根据每个样本与其最近邻类标的异同决定其取舍，然后再用SVM训练得到分类器．实验表明，NN-SVM相比SVM在分类正确率、分类速度以及适用的样本规模上都表现出了一定的优越性．

An Improved SVM: NN-SVM

A support vector machine constructs an optimal hyperplane from a small set of samples near the boundary. This makes it sensitive to these specific samples and tends to result in machines either too complex with poor generalization ability or too imprecise with high training error, depending on the kernel parameters. SVM focuses on the samples near the boundary in training time, and those samples intermixed in another class are usually no good to improve the classifier's performance, instead they may greatly increase the burden of computation and their existence may lead to overlearning and decrease the generalization ability. In order to improve the generalization ability we present an improved SVM: NN-SVM. It first prunes the training set, reserves or deletes a sample according to whether its nearest neighbor has same class label with itself or not, then trains the new set with SVM to obtain a classifier. Experiment results show that NN-SVM is better than SVM in speed and accuracy of classification.