Abstract
Support Vector Machine is one of the classical machine learning techniques that can still help solve big data classification problems. Especially, it can help the multidomain applications in a big data environment. However, the support vector machine is mathematically complex and computationally expensive. The main objective of this chapter is to simplify this approach using process diagrams and data flow diagrams to help readers understand theory and implement it successfully. To achieve this objective, the chapter is divided into three parts: (1) modeling of a linear support vector machine; (2) modeling of a nonlinear support vector machine; and (3) Lagrangian support vector machine algorithm and its implementations. The Lagrangian support vector machine with simple examples is also implemented using the R programming platform on Hadoop and non-Hadoop systems.
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Acknowledgements
I would like to thank Professor Vaithilingam (Jeya) Jeyakumar of the University of New South Wales, Australia, for giving me an opportunity to work with him and his research team on support vector machine problems and associated implementations to different applications. I also participated in the research focusing on enhancing the support vector machine technique and published our theory, results, and findings. This research contributed to this chapter.
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Suthaharan, S. (2016). Support Vector Machine. In: Machine Learning Models and Algorithms for Big Data Classification. Integrated Series in Information Systems, vol 36. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7641-3_9
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DOI: https://doi.org/10.1007/978-1-4899-7641-3_9
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