A Comparison Study: Support Vector Machines for Binary Classification in Machine Learning
This paper focuses on the fundamental interpretation of various kinds of SVM which achieve comparable performance in dealing with different data sets of two classes in machine learning. Based on the fundamental of original SVM, L2RL2SVM, C-SVC, L1RL2SVM and v-SVC can linearly handle the largescale high-dimension data well. When the training data can not be separated by linear classifiers, KSVM is a crucial technique to overcome this shortage in a supervised-learning framework. In a semi-supervised learning framework, LapSVM, is the state-of-the-art technique which better handles both generalization of the classifier and out-of-sample problem. Experiments, on four common datasets of two classes with eleven algorithms, show that accuracies, utilizing different algorithms in different data sets, are different and prior geometrical information from training data is necessary to improve the accuracy when the machine learns to select a optimum model either in a supervised-learning framework or in a semi-supervised-learning framework. By analyzing the results of the experiments, we found that the distributional characteristics of each data set can be observed.
Wencai Zeng Jiong Jia Zhonglong Zheng Chenmao Xie Li Guo
Department of Computer Science, Zhejiang Normal University, Jinhua,China,321004
国际会议
上海
英文
1633-1637
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）