A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Using Model Trees for Classification
Machine Learning
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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In this paper, a new method to solve the classified problems by using Support Vector Regression is introduced. Proposed method is called as SVR-C for short. In the method, through reconstructing the training set, each class through reconstructing the training set, each class value corresponding to a new training set, then use the SVR algorithm to train it and get a constructed model. And then, to a new instance, use the constructed model to train it and approximate the target class to the maximization of output value. Compared with M5P-C, SMO, J48, the effectiveness of our approach is tested on 16 publicly available datasets downloaded from the UCI. Comprehensive experiments are performed, and the results show that the SVR-C outperforms M5P-C and J48, and takes on comparative performance to SMO but has low standard-deviation. Moreover, our approach performs well on multi-class problems.