关键词: 支持向量机 多层分类 Half-Against-Half

Abstract:

A Half-Against-Half （HAH） multi-class SVM is proposed in this paper. HAH is built via recursively dividing the training dataset of K classes into two subsets of classes. The structure of HAH is the same as a decision tree with each node as a binary SVM classifier that tells a testing sample to belong to a certain group of classes. Unlike the commonly used One-Agalnst-All （OVA） and One-Against-One （OVO） implementation methods, both the theoreticcal estimation and experimental results show that HAH is superior to OVA,OVO and DAG, and is supported by the experimental data in practice, especially    in the training time, speed, and training set sizes.

Key words: SVM（support vector machine）, multi-level classification, Half-Against-Half