Chongming Wu

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Support Vector Machines (SVM) are the classifiers which were originally designed for binary classification. The classification applications can solve multi-class problems. Decision-tree-based support vector machine which combines support vector machines and decision tree can be an effective way for solving multi-class problems. This method can decrease the(More)
Decision-tree-based support vector machine which combines support vector machines and decision tree is an effective way for solving multi-class problems. A problem exists in this method is that the division of the feature space depends on the structure of a decision tree, and the structure of the tree relate closely to the performance of the classifier. To(More)
To reduce the computational cost of the incremental learning, a fast SVM incremental learning algorithm based on the convex hulls algorithm is proposed in this paper. The given algorithm is based on utilizing the result of the previous training effectively and retaining the most important samples for the incremental learning to reduce the computational(More)
Based on analyzing the relationship between the Karush-Kuhn-Tucker (KKT) conditions of support vector machine and the distribution of the training samples, the possible changes of support vector set after new samples are added to training set was analyzed, and the generalized Karush-Kuhn-Tucker conditions was defined. Based on the classification equivalence(More)
Support vector machine (SVM) has been used in high resolution range profile (HRRP) classification for its good generalization ability for the pattern classification problem with high feature dimension and small training set. In order to perform multi-class classification, decision-tree-based SVM was studied, the structure and the classification performance(More)
How to deal with the newly added training samples, and utilize the result of the previous training effectively to get better classification result fast are the main tasks of incremental learning. A fast SVM incremental learning algorithm based on the central convex hulls algorithm is proposed in this paper. To utilize the result of the previous training and(More)
The relation between the performance of AdaBoost and the performance of base classifiers was analyzed, and the approach of improving the classification performance of AdaBoostSVM was studied. There is inconsistency existed between the accuracy and diversity of base classifiers, and the inconsistency affect generalization performance of the algorithm. A new(More)
Radar target identification by using high resolution range profile (HRRP) have been studied extensively. Effective way of HRRP classification by support vector machine (SVM) was studied in this paper. In order to improve the classification performance of SVM, the approach of improving the classification performance of AdaBoostSVM was studied. A new variable(More)
By choosing the most informative patterns that have the most possibility to become the support vectors in the training data by using the convex hulls algorithm, a fast training algorithm for SVM (QhullSVM) is given in this paper. Experimental results reveal that the given QhullSVM has better training performance comparing with the traditional training(More)