Abstract
Support vector machines (SVMs) are a class of machine learning algorithms which use kernel functions to map data into feature space, where a separating hyperplane can be computed to classify the data. Generally, SVMs are reliant on the selection of kernel function and parameters, which can severely downgrade the performance of the classifier if mishandled. In this paper, we compare the performance of the Euclidean-SVM (ESVM), that have low dependency on kernel selection, with conventional SVM. The comparison are conducted with two different datasets. Results show that the ESVM has higher performance consistency as compared to SVM.
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Acknowledgement
The work is supported by the Fundamental Research Grant Scheme (FRGS) by the Ministry of Higher Education, Malaysia (FRGS/1/2018/ICT02/UNIM/02/4).
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Hoong, K.T.K., Wan, W.Y., Tien, M.T.T., Nugroho, H. (2022). A Comparative Analysis of Euclidean-Support Vector Machine. In: Mahyuddin, N.M., Mat Noor, N.R., Mat Sakim, H.A. (eds) Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 829. Springer, Singapore. https://doi.org/10.1007/978-981-16-8129-5_91
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DOI: https://doi.org/10.1007/978-981-16-8129-5_91
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