A Radial Basis Function Network (RBFN) is a particular type of neural network. In this article, I’ll be describing it’s use as a non-linear classifier.
Generally, when people talk about neural networks or “Artificial Neural Networks” they are referring to the Multilayer Perceptron (MLP). Each neuron in an MLP takes the weighted some of its input values. That is, each input value is multiplied by a coefficient, and the results are all summed together. A single MLP neuron is a simple linear classifier, but complex non-linear classifiers can be built by combining these neurons into a network.
To me, the RBFN approach is more intuitive than the MLP. An RBFN performs classification by measuring the input’s similarity to examples from the training set. Each RBFN neuron stores a “prototype”, which is just one of the examples from the training set. When we want to classify a new input, each neuron…
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