多分类簇支持向量机方法

针对支持向量机的多分类问题，提出一种新颖的基于非平行超平面的多分类簇支持向量机。它针对k模式分类问题分别训练产生k个分割超平面，每个超平面尽量靠近自身类模式而远离剩余类模式；决策时，新样本的类别由它距离最近的超平面所属的类决定，克服了一对一（OAO）和一对多（OAA）等传统方法存在的“决策盲区”和“类别不平衡”等缺陷。基于UCI和HCL2000数据集的实验表明，新方法在处理多分类问题时，识别精度显著优于传统多分类支持向量机方法。

Multi-class cluster support vector machines

Based on the idea of nonparallel hyperplanes,a novel multi-class cluster support vector machine method was proposed to settle the multi-class classification problem of support vector machines.For a k-class classification problem,it trained k-hyperplanes respectively,and each one lay as close as possible to self-class while being apart from the rest classes as far as possible.Then,labels of new samples were determined by the class of their nearest hyperplane,thus the inherent limitations of One-Against-One(O...