支向機(Support Vector Machines, SVM)原本用於處理二類別分類問題,但在許多應用領域所需處理的問題為多類別分類問題;因此如何將二類支向機之觀念,有效延伸為多類支向機是目前研究的重要課題。本文先針對五種常見的多類支向機方法簡略介紹,再提出一種新的多類支向機:one-against-half method,並以實際資料庫測試,比較此方法和其它多類支向機處理分類問題的效果。經實驗結果得知,one-against-half method亦為處理多類別分類問題時,可以選用的分類方法之一。
Support vector machines (SVM) was originally designed for binary classification. SVM has been recently applied to solve multi-class problems. And there lies the unsolving research issues on developing 2-class SVM into multi-class SVM. In this paper, five common multi-class SVMs have been reviewed and a new multi-class SVM "one-against-half method" has been proposed along with the comparison between the performance of one-against-half method and the other five multi-class SVMs. The experiments proved one- against-half method to be a qualified multi-class SVM.