Radial basis function

Radial Basis Functions
A radial basis function is simply a gaussian, . It is called local because, unlike the previous functions, it is essentially zero everywhere except in a small region.
> f5 := proc(x,a) exp(-a * x^2); end; ...

Radial Basis Function regression Basis function regression where each new feature is based on the distance to a prototype, hence the basis is "radial." The resulting curve is a superposition of "bumps," one at each prototype.

Radial Basis Function Networks
We have seen in the last section how an MLP models the response function using the composition of sigmoid-cliff functions - for a classification problem, ...

Radial basis function
Radial basis function network
Random neural network
Recurrent neural network
Reservoir computing
Rprop ...

Radial basis function networks are also feedforward, but have only one hidden layer.
Typical RBF architecture:
Like BP, RBF nets can learn arbitrary mappings: the primary difference is in the hidden layer.

2] Gaussian Radial Basis Function: Radial basis functions most commonly with a Gaussian form ...

Back-Propagated Delta Rule Networks (BP) (sometimes known and multi-layer perceptrons (MLPs)) and Radial Basis Function Networks (RBF) are both well-known developments of the Delta rule for single layer networks (itself a development of the ...

Next they take this massive correlation matrix and use a support vector machine (SVM with soft margin, including a radial basis function "kernel trick") to classify each timeseries as belonging to a child (7-11 years old) or an adult ...

(Kiyan and Yildirim, 2003) employed Radial Basis Function, General Regression Neural Network and Probabilistic Neural Network in order to get the suitable result.

We talk about linear regression, and then these topics: Varying noise, Non-linear regression (very briefly), Polynomial Regression, Radial Basis Functions, Robust Regression, Regression Trees, Multilinear Interpolation and MARS.

Kohonen feature maps are often used for unsupervised learning and clustering and Radial Basis Function networks are used for supervised learning and in some ways represent a hybrid between nearest neighbor and neural network classification.

Kernel regression is a superset of local weighted regression and closely related to Moving Average and K nearest neighbor (KNN), radial basis function (RBF), Neural Network and Support Vector Machine (SVM).

Feedforward neural networks with a maximum of 512 input neurons and three hidden layers.
The activation function of the neurons can be programmed in a lookup table.
Kohonen feature maps and radial basis function networks also implemented.

See also: Neural network, Regression, Classification, Distribution, Percept