Abstract
Support vector machine (SVM) is one of the most successful machine learning algorithms to solve practical pattern classification problems. The selection of the kernel function and its parameter plays a vital role on the results. Radius basis function (RBF) is a prevalently used kernel. For an RBF-SVM, two parameters, c and \(\gamma \), control the SVM performance. In this paper, we present a SVM parameter learning algorithm, DL&BA, effective for learning from big data. The DL&BA algorithm has two stages. At the first stage, we use a distributed learning (DL) to search for a region which promises optimal parameter pairs. At the second stage, a swarm intelligent optimization algorithm - the Bees Algorithm (BA) is used to search for an optimal pair of c and \(\gamma \). We applied the DL&BA algorithm to solving an important automotive safety problem, driver fatigue detection, which involves a large amount of real-world driving data. Our experimental results show that DL&BA is not only computational efficient but also effective in finding an optimal pair of c and \(\gamma \).
This work is supported in part by research grants from Michigan Institute of Data Science (MIDAS), ZF-TRW, and Ford Motor Company.
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Xie, Y., Murphey, Y.L., Kochhar, D.S. (2018). SVM Parameter Optimization Using Swarm Intelligence for Learning from Big Data. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11055. Springer, Cham. https://doi.org/10.1007/978-3-319-98443-8_43
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