**5. Classification using machine learning techniques**

## **5.1 Support vector machine**

Support Vector Machine (SVM) is a discriminative classifier algorithm, concerning the patterns represented by the subset *di* ¼ þ1 and the patterns represented by the subset *di* ¼ �1 are linearly separable. The decision surface that is in the form of a hyperplane that does the separation is given as follows.

$$\boldsymbol{w}^T \mathbf{x} = \sum\_{i} w\_i \mathbf{x}\_i \tag{2}$$

$$\mathbf{w}^T \mathbf{x} + \mathbf{b} = \mathbf{0} \tag{3}$$

where *x* is an input vector, *w* is an adjustable weight vector, and *b* is bias. The point closest to the hyperplane is called the 'support vector'. The SVM classifier maximizes

the margin of separation between the classes and minimizes the classification errors [28, 29]. The best hyperplane for SVM is the one with the largest margin between the two classes.
