Statistically speaking, the nonlinear regression is a kind of regression analysis used to estimate the relationships between one or more independent variables in a nonlinear combination.
In this chapter, we will use the mlpy Python library, 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 (KRRKernel Ridge Regression (KRR), which is a combination of Ridge Regression using a small kernel trick that corresponds to a nonlinear function that fits a line to some values mapped from X to Y. It is similar to a Support Vector Machines (SVM), as we will see in Chapter 8, Working with Support Vector Machines, but the solution depends on all the training samples and not on a subset of support vectors. KRR works well with a few training sets for classification and regression. It is widely...