8Support vector machines algorithms
This chapter covers the following items:
–Algorithm for support vectors
–Algorithm for classifying linear and nonlinear
–Examples and applications
Support vector machines (SVMs) are used as a method for classifying linear and nonlinear data. This chapter discusses on how SVM works. It basically utilizes a nonlinear mapping for converting the original training data into a higher dimension in which it finds the linear optimal separating hyperplane (a “decision boundary” splitting the datasets of a class from another one). If an appropriate nonlinear mapping is used with an adequately high dimension, data from two classes can at all times be split by a hyperplane [1–5]. Through the use of support vectors (“essential” ...
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