Abstract
The efficient classification ability of support vector machine (SVM) has been shown in many practical applications, but currently it is significantly slower in testing phase than other classification approaches due to large number of support vectors included in the solution. Among different approaches, simplification of support vector machine (SimpSVM) accelerates the test phase by replacing original SVM with a simplified SVM that uses significantly fewer support vectors. Nevertheless, the final aim of the simplification is to try keeping the simplified solution as similar as possible to the original one. To ameliorate this similarity, in this paper, we present a modification of solution optimization in SimpSVM. The proposed approach is based on stochastic optimization. Experiments on benchmark and sign language datasets show improved results by our modification.
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Thang, P.Q., Thuy, N.T., Lam, H.T. (2018). A Modification of Solution Optimization in Support Vector Machine Simplification for Classification. In: Bhateja, V., Nguyen, B., Nguyen, N., Satapathy, S., Le, DN. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 672. Springer, Singapore. https://doi.org/10.1007/978-981-10-7512-4_15
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DOI: https://doi.org/10.1007/978-981-10-7512-4_15
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