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