摘要
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。
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 （OAO） and One-Against-All （OAA） methods can be avoided, such as ＂decision blind-area＂ and ＂unbalanced classes＂. Finally, experiments on UCI and HCL2000 datasets show that the proposed method significantly outperforms traditional OAO and OAA in terms of recognition accuracy.
出处
《计算机应用》
CSCD
北大核心
2010年第1期143-145,149,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(69732010)
西南财经大学科学研究基金资助项目(QN0806)
关键词
支持向量机
超平面
核函数
手写体汉字识别
Support Vector Machine （SVM）
hyperplane
kernel function
handwritten Chinese character recognition
