Summary
In this chapter, we get into dimensionality reduction and linear classification using SVM. In our example, we create a simple but powerful SVM classifier and we learn how to perform dimensionality reduction using PCA implemented in Python with mlpy. Finally, we present how to use non-linear kernels, such as Gaussian or Polynomial. The work in this chapter is just an introduction to the SVM algorithm with only two dimensions, the results can be improved with a multidimensional approach.
In the next chapter, we will learn how to model an epidemiological event (an infectious disease), and how to simulate an outbreak with cellular automation implemented in D3.js.
