In this chapter, we will use the Python library
mlpy and its Kernel ridge regression implementation. We can find more information about nonlinear regression methods at http://mlpy.sourceforge.net/docs/3.3/nonlin_regr.html.
The most basic algorithm that can be kernelized is Kernel ridge regression (KRR). It is similar to an SVM (Support Vector Machines) (see Chapter 8, Working with Support Vector Machines) but the solution depends on all the training samples and not on the subset of support vectors. KRR works well with few training sets for classification and regression. In this chapter, we will focus on its implementation using
mlpy rather than all the linear algebra involved. See Appendix, Setting Up the Infrastructure, for complete installation instructions for