Radial Basis Function

Radial Basis Function (RBF) is a mathematical function that maps input data to a higher dimensional space, allowing for non-linear classification and regression. In machine learning, RBF is commonly used as a kernel function in support vector machines (SVMs) and as a basis function in neural networks. The RBF kernel measures the similarity between two data points in the transformed space, where the distance between the points is calculated using a Gaussian distribution. RBF networks are a type of neural network that use RBFs as activation functions in the hidden layer. These networks are particularly useful for function approximation and pattern recognition tasks.
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