Theoretical aspects of mapping to multidimensional optimal regions as a multi-classifier
Intelligent Data Analysis
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Combined by several binary-class SVMs, present Multi-Class SVMs are usually inefficient in training process. When there is large number of categories of data to classify, training it would be very difficult. Expanded from Hyper-Sphere One-Class SVM (HSSVM), Hyper-Sphere Multi-Class SVM (HSMC-SVM), which builds a HS-SVM for every category of data, is a direct classifier. Its training speed is faster than the combined Multi-Class classifiers. In order to fast train the HSMC-SVM, a training algorithm following the idea of SMO is proposed. For researching the generalization performance of HSMC-SVM, the theoretic upper bound of generalizing error of HSMC-SVM is analyzed too. As shown in the numeric experiments, the training speed of HSMC-SVM is faster than 1-v-r and 1-v-1, but the classification precision is lower than them. HSMC-SVM provides a new idea on researching fast directed multi-class classifiers in machine learning area.