Solving large-scale multiclass learning problems via an efficient support vector classifier
Shuibo Zheng, Tang Houjun, Han Zhengzhi, Haoran Zhang · Journal of Systems Engineering and Electronics · 2006
Support vector machines (SVMs) are initially designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research topic. A multiclass classifier is constructed by combining SVMlightalgorithm with directed acyclic graph SVM (DAGSVM) method, named DAGSVMlightA new method is proposed to select the working set which is identical to the working set selected by SVMlightapproach. Experimental results indicate DAGSVMlightis competitive with DAGSMO. It is more suitable for practice use. It may be an especially useful tool for large-scale multiclass classification problems and lead to more widespread use of SVMs in the engineering community due to its good performance.