Abstract
Besides linear classification, Support Vector Machine (SVM) is proficient in non-linear classification by deploying kernel tricks that implicitly maps and transform input features to high dimensional feature space. Kernel-SVM, can be utilized to secure progressively complex connections on datasets with no push to do changes all alone. In this paper, 5 different SVM kernel functions are implemented on 4 datasets, viz., IRIS, Breast Cancer Wisconsin (diagnostic), Mushroom and Letter Recognition Dataset. The five kernel functions considered in this paper are: Linear kernel, Gaussian Radial Basis Function (RBF) kernel, Laplacian kernel, Polynomial kernel and Sigmoid kernel. Our goal is to locate the best non-linear kernel. The outcomes show that the precision of expectation for Laplacian kernel is most extreme with a forecast scope of (max 100%, min 97.53%) and least for the sigmoid kernel with a forecast scope of (max 100%, min 47.28%).
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Virmani, D., Pandey, H. (2023). Comparative Analysis on Effect of Different SVM Kernel Functions for Classification. In: Gupta, D., Khanna, A., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 492. Springer, Singapore. https://doi.org/10.1007/978-981-19-3679-1_56
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DOI: https://doi.org/10.1007/978-981-19-3679-1_56
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