RadialbasisFun

RadialbasisFun is a term that can refer to a radial basis function (RBF) network or the mathematical concept of radial basis functions themselves. Radial basis functions are real-valued functions whose value depends only on the distance from some origin point. A common example is the Gaussian function, where the value decreases as the distance from the center increases.

In machine learning, radial basis function networks are a type of artificial neural network that uses radial basis functions as activation functions. These networks typically have an input layer, a hidden layer containing radial basis functions, and an output layer. The hidden layer neurons compute the distance between the input vector and their respective center vectors, then apply a radial basis function to this distance. The output layer then combines the outputs of the hidden layer neurons, often through a linear combination, to produce the final prediction.

RBF networks are known for their ability to approximate complex, non-linear functions and are often used for tasks such as function approximation, classification, and time series prediction. They can be computationally efficient for certain problems and are relatively easy to train. The choice of the radial basis function and its parameters, such as the centers and widths of the basis functions, significantly impacts the network's performance.