Abstract
This chapter covers another major and powerful machine learning tool which is SVM . The chapter begins with the introduction of linear classifier , K-NN classifier, and perceptron which are the key to understand discriminative approaches such as SVM and ANN. After these preparations, the primal form SVM is formally introduced. It then goes on to transform primal form SVM to dual form SVM with detailed explanation and justification. In the next, kernel SVM is formally defined and techniques of building new kernels are described in details. For computation purpose, the kernel trick is introduced and an application of SVM using PMK is also demonstrated. The chapter concludes with three fusion techniques to build multi-class SVM classifiers.
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Zhang, D. (2019). Support Vector Machine. In: Fundamentals of Image Data Mining. Texts in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-17989-2_8
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DOI: https://doi.org/10.1007/978-3-030-17989-2_8
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