Numerical Machine Learning

Support Vector Machine

Author(s): Zhiyuan Wang*, Sayed Ameenuddin Irfan*, Christopher Teoh* and Priyanka Hriday Bhoyar *

Pp: 160-193 (34)

DOI: 10.2174/9789815136982123010008

* (Excluding Mailing and Handling)


In this chapter, we investigate Support Vector Machines (SVM) for both linearly separable and linearly non-separable cases, emphasizing accessibility by minimizing abstract mathematical theories. We present concrete numerical examples with small datasets and provide a step-by-step walkthrough, illustrating the inner workings of SVM. Additionally, we offer sample codes and comparisons with the SVM model available in the scikit-learn library. Upon completing this chapter, readers will gain a comprehensive understanding of SVM's mechanics, and its connection to the implementation and performance of the algorithm, and be well-prepared to apply it in their practical applications.

Keywords: Support Vector Machine, Linearly Separable, Linearly Non-Separable, Polynomial Kernel, Radial Basis Function Kernel, Numerical Example, Small Dataset, Scikit-Learn

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