Radial Basis Functions, RBF Kernels, & RBF Networks Explained Simply
A different learning paradigm
Here is a set of one-dimensional data: your task is to find a way to perfectly separate the data into two classes with one line.
At first glance, this may appear to be an impossible task, but it is only so if we restrict ourselves to one dimension.
Let’s introduce a wavy function f(x) and map each value of x to its corresponding output. Conveniently, this makes all the blue points higher and the red points lower at just the right locations. We can then draw a horizontal line that cleanly divides the classes into two parts.
This solution seems very sneaky, but we can actually generalize it with the help of radial basis functions (RBFs). Although they have many specialized use cases, an RBF inherently is simply a function whose points are defined as distances from a center. Methods that use RBFs fundamentally share a learning paradigm different from the standard machine learning fare, which is what makes them so powerful.
For example, the Bell Curve is an example of a RBF, since points are represented as number of…
