摘要
本文提出了一种多类SVM分类器——ACDMSVM,它是基于决策有向无环图和积极约束的多类SVM分类器。对于k类问题,它将k(k-1)/2个改进的二类SVM分类器进行组合。为了提高分类器的训练及决策速度,对标准的二类SVM分类器进行三个方面的改进:利用大间隔方法,对软间隔错误变量采用2-范数形式并应用积极约束。在训练阶段,使用含有根的二元有向无环图进行节点的选择,该有向无环图含k(k-1)/2个内部节点和k个叶节点。数值实验表明这是一种快速的多类SVM分类器。
This paper presents a new method of constructing multi-class SVM classifier- ACDMSVM, which is based on the structure of Decision Directed Acyclic Graph(DDAG) and using active constraint for each SVM classifier. For k -class problem, it combines k (k- 1)/2 two-class SVM classifiers, one for each pair of classes. In order to speed up the training and decision process of the classifier, we make three changes on the standard two-class classifiers, i.e. large margin, 2-norm squared for the error for the soft margin and active constraint. While in the testing phase, we use a rooted binary directed acyclic graph which has k (k - 1) /2 internal nodes and k leaves. Computational experiment indicates that it is a simple and fast approach to generating multi-class SVM classifiers.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2003年第2期164-168,共5页
Pattern Recognition and Artificial Intelligence
关键词
机器学习
支持向量机
有向无环图
ACDMSVM
多类SVM分类器
Support Vector Machine, Multi-Class Classification Problem, Active Constraint, Decision Directed Acyclic Graph
