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- W1489494530 abstract "Support Vector Machine (SVM) employs Structural Risk Minimization (SRM) principle to generalize better than conventional machine learning methods employing the traditional Empirical Risk Minimization (ERM) principle. However, training SVM requires large memory and long cpu time when training set is large. One way to circumvent this computational burden is to select some of training patterns in advance which contain most information given to learning. One of the merits of SVM theory distinguishable from other learning algorithms is that it is clear that which patterns are of importance to training. Those are called support vectors (SVs), distributed near the decision boundary, and fully and succinctly define the classification task at hand. Furthermore, on the same training set, the SVMs trained with different kernel functions, i.e., RBF, polynomial, and sigmoid, have selected almost identical subset as support vectors. Therefore, it is worth finding such would-be support vectors prior to SVM training. In the thesis, we propose neighborhood property based pattern selection algorithm (NPPS) which selects the patterns near the decision boundary based on the neighborhood properties. We utilizes k nearest neighbors to look around the pattern’s periphery. The first neighborhood property is that “a pattern located near the decision boundary tends to have more heterogeneous neighbors in their class-membership.” The second neighborhood property dictates that “an overlap or a noisy pattern tends to belong to a different class from its neighbors.” The first one is used for identifying those patterns located near the decision boundary. The second one is used for removing the patterns located on the wrong side of the decision boundary. These properties are first implemented as a naive form with time complexity O(M) where M is the number of given training patterns. Then, to accelerate the pattern selection procedure we utilize another property. The third neighborhood property is that “the neighbors of a pattern located near the decision boundary tend to be located near" @default.
- W1489494530 created "2016-06-24" @default.
- W1489494530 creator A5043830800 @default.
- W1489494530 date "2005-02-01" @default.
- W1489494530 modified "2023-09-27" @default.
- W1489494530 title "Efficient Pattern Selection for Support Vector Classifiers and its CRM Application" @default.
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