## Support vector classifiers – SVC

Support vector machines can be applied to classification, anomalies detection, and regression problems. Let's first dive into the support vector classifiers.

### The binary SVC

The first classifier to be evaluated is the binary (2-class) support vector classifier. The implementation uses the LIBSVM library created by Chih-Chung Chang and Chih-Jen Lin from the National Taiwan University [8:9].

#### LIBSVM

The library was originally written in C before being ported to Java. It can be downloaded from http://www.csie.ntu.edu.tw/~cjlin/libsvm as a `.zip`

or `tar.gzip`

file. The library includes the following classifier modes:

Support vector classifiers (C-SVC, υ-SVC, and one-class SVC)

Support vector regression (υ-SVR and ε-SVR)

RBF, linear, sigmoid, polynomial, and precomputed kernels

LIBSVM has the distinct advantage of using **Sequential Minimal Optimization** (**SMO**), which reduces the time complexity of a training of *n* observations to *O(n
^{2})*. The LIBSVM documentation covers both the...