Radial Basis Function (RBF) Networks

Radial Basis Function (RBF) networks are a type of artificial neural network used for pattern recognition and function approximation. They work by transforming input data into a space where it becomes easier to distinguish different patterns, using a special function that responds strongly to inputs close to certain points. These functions serve as building blocks that analyze the similarity between new data and known examples. The network then combines these responses to make predictions or classifications. Overall, RBF networks are valued for their ability to quickly and accurately model complex relationships in data.