Reducing the Size of Very Large Training Set for Support Vector Machine Classification

نویسندگان

  • Mahmoudreza Ahmadi
  • Hamidreza Ghaffari
چکیده

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.

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تاریخ انتشار 2014