Chapter 8. Working with Support Vector Machines

The support vector machine (SVM) is a powerful classification technique. In this chapter, we will provide the reader with an easy way to get acceptable results using SVM. We will perform dimensionality reduction of the dataset and we will produce a model for classification.

The theoretical foundation of SVM lies in the work of Vladimir Vapnik and the theory of statistical learning developed in the 1970s. The SVMs are highly used in pattern recognition of Time Series, Bioinformatics, Natural Language Processing, and Computer Vision.

In this chapter, we will use the mlpy implementation of LIBSVM, which is a widely used library for SVM with several interfaces and extensions for languages such as Java, Python, MATLAB, R, CUDA, C#, and Weka. For more information about LIBSVM visit the following link:

http://www.csie.ntu.edu.tw/~cjlin/libsvm/

In this chapter we will cover:

• Understanding the multivariate dataset

• Dimensionality Reduction

  • Linear Discriminant...