不等距超球体支持向量机

针对现实中经常遇到的各类样本分布范围相差很多、将各类样本误判的危害程度不同、或者各类样本数量差异悬殊等情况，提出了一种基于不等距超球体的SVM（NMS-SVM）算法。该算法以最大间隔为优化目标建立分类模型，同时引入距离比例参数λ，调整最优分类面到两类之间的距离。通过UCI数据库中数据集的分类仿真实验，比较了该算法与普通超球体算法以及最大间隔超球体算法的分类精度，证明了该算法的有效性。

Non-equidistant margin hypersphere SVM

For different kinds of samples,the following cases are often confronted：The scopes of sample-distribution differ largely,the degrees of harm for misjudgment of sample class are different,or,the numbers of different kinds of samples are various,pointed on which,SVM（NMS-SVM） based on the non-equidistant margin hypersphere is proposed.Aim at optimizing the maximal margin,SVM introduces distance ratio parameter and adjusts the distance between the optimized classing surface and the two classes.Through the simulation experiment of classification of sample collection by UCI corpus,the validity of SVM is proved by comparing the precision of classification with the normal hypersphere arithmetic as well as the maximal margin hypersphere arithmetic.