What are the main differences between two types of feedforward networks such as multilayer perceptrons (MLP) and radial basis function (RBF)?
What are the fundamental differences between these two types?
RBF and MLP belong to a class of neural networks called feed-forward networks.
Hidden layer of RBF is different from MLP. It performs some computations. Each hidden unit act as a point in input space and activation/output for any instance depends on the distance between that point(Hidden Unit) and instance(Also a point in space).
Thus, RBF learns two kinds of parameters:
1) Centers and width of RBFs
2) Weights to from linear combination of outputs obtained from hidden layer
The first set of parameters can be learned independently from the second set of parameters. i.e. by performing K-means clustering, you can come with centers and width of RBFs.
In nutshell, RBFs use the distance between RBFs centers and instances as the similarity measure.
One disadvantage is RBFs give the same weight to every attribute as they are considered equally in the distance computation.