中文摘要：

针对投影孪生支持向量机（Projection Twin Support VectorMachine,PTSVM）在训练和求解过程中存在的问题,提出了一类改进的投影孪生支持向量机（Improved PTSVM）,简称为IPTSVM.该文首先构造了改进的线性投影孪生支持向量机,然后利用核技巧轻松将其推广到了非线性形式.本文的主要贡献有：（1）提出了投影孪生支持向量机的新模型,克服了原始PTSVM在训练之前需要求解两个逆矩阵的问题;（2）继承了传统SVM（Support VectorMachine）的精髓,利用核技巧直接将线性IPTSVM推广到非线性形式;（3）引入了一个新的参数,可以调节模型的性能,提高了IPTSVM的分类精度.实验结果表明,与PTSVM算法相比较,IPTSVM不仅提高了分类精度,而且克服了PTSVM的一些不足.

英文摘要：

An Improved Projection Twin Support Vector Machine（IPTSVM） is presented.The target of the proposed IPTSVM is to deal with a set of problems in the training and solving steps of PTSVM.We first propose a linear IPTSVM for binary classification.Then we extend it to the corresponding nonlinear version using kernel tricks.The paper has three main contributions to the community：（1） A new PTSVM-based method is proposed,in which we do not have to compute the inverse of a large matrix before the training step.（2） We design the nonlinear IPSVM that is obtained by using kernel tricks.（3） A new parameter is introduced,which can adjust the performance of the model and improve the classification accuracy of IPTSVM.Experimental results obtained from several datasets demonstrate that,compared with PTSVM,IPTSVM not only improves the classification accuracy but also overcomes some deficiencies to a certain extent.