Solving large-scale multiclass learning problems via an efficient support vector classifier

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 SVM
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algorithm with directed acyclic graph SVM (DAGSVM) method, named DAGSVM
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A new method is proposed to select the working set which is identical to the working set selected by SVM
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approach. Experimental results indicate DAGSVM
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is 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.