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Support Vector Machine Based on a New Reduced Samples Method

Author: MengJie
Tutor: LuShuXia
School: Hebei University
Course: Applied Mathematics
Keywords: Support Vector Machine Support Vector Domain Description Inner product Reduce
Type: Master's thesis
Year: 2011
Downloads: 31
Quote: 0
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Support Vector Machine (SVM) founded on Vapnik statistical learning theory, a novel machine learning method to the small datasets, have played an important role in many areas, due to its salient properties such as margin maximization and kernel substitution for classifying the data in high dimensional feature space. Besides, SVMs have high fitting accuracy, a small number of tunable parameters and can find the global solution.Nevertheless, for the large scale dataset, the speed of SVM is very slow because of its great memory space and its large amount of calculation. For the problem of many non-support vectors and a few support vectors in the classification of SVM, a method to reduce the samples that may be not support vectors is proposed in this paper. Firstly adopt Support Vector Domain Description to find the smallest sphere containing the most of data points, remove some objects outside the sphere based on the knowledge of the inn dot, and then based on the distance of each pattern to the centers of other classes to remove the edge points.In comparison with the standard SVM, the experimental results show that the new algorithm in the paper is capable of reducing the number of samples as well as the training time while maintaining high accuracy,especially for the large number datasets, the new method is more suitable.

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
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