Describe the differences between backpropagation and
radial basis function learning networks?
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Answer / Indrajeet Kumar Yadav
Backpropagation is a supervised learning algorithm used in feedforward artificial neural networks. It calculates the gradient of the error function with respect to the weights using chain rule, and then adjusts these weights iteratively to minimize the error. Radial Basis Function (RBF) networks are also supervised learning algorithms but have a different architecture compared to backpropagation networks. RBF networks use a layer of hidden nodes that map the input space to a high-dimensional feature space using radial basis functions, and then a simple linear regression or classification algorithm is applied in the output layer. The main difference between them lies in their structure, learning process, and performance on certain types of problems.
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