文章基本信息

标题：Reducing the Size of Very Large Training Set for Support Vector Machine Classification
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作者：Mahmoudreza Ahmadi ; Hamidreza Ghaffari
期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2014
卷号：4
期号：5
页码：55-61
出版社：International Journal of Soft Computing & Engineering
摘要：Normal support vector machine (SVM) algorithms are not suitable for classification of large data sets because of high training complexity. In this paper, we introduce a method based on edge recognition technique to find low-value data, where to keep input data distribution, we use clustering algorithm like k-means to compute clusters centers. Data is selected through edge recognition algorithm and cluster centers, are used to build a training data set. Reconstructed data set with small size, increase the speed of training process procedure without decreasing classification precision. But, as we used k-means algorithm, it is required to initially specify the number of classes. We try to get a proper procedure by improving edge recognition algorithm to reduce data, also using hierarchical clustering algorithm and similarity percent to compute number of clusters instead of using k-means algorithm, and compare results of these two algorithms.
关键词：Support vector machine; k-means; optimization;edge recognition; cluster; hierarchical; similarity percent