Support vector machine (SVM) is one of the top picks in pattern recognition and classification related tasks. It has been used\nsuccessfully to classify linearly separable and nonlinearly separable data with high accuracy. However, in terms of classification\nspeed, SVMs are outperformed by many machine learning algorithms, especially, when massive datasets are involved. SVM\nclassification speed scales linearly with number of support vectors, and support vectors increase with increase in dataset size.\nHence, SVM classification speed can be enormously reduced if it is trained on a reduced dataset. Instance selection techniques\nare one of themost effective techniques suitable for minimizing SVM training time. In this study, two instance selection techniques\nsuitable for identifying relevant training instances are proposed. The techniques are evaluated on a dataset containing 4000 emails\nand results obtained compared to other existing techniques. Result reveals excellent improvement in SVM classification speed.
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