![]() The Gaussian function is complicated and includes many terms we’ll dig into each of them in the following sections. This produces the familiar bell curve shown below, which is centered at the mean, mu (in the below plot the mean is 5 and sigma is 1). Where x is the input, mu is the mean, and sigma is the standard deviation. Below is the equation for a Gaussian with a one-dimensional input. There are different possible choices of similarity functions, but the most popular is based on the Gaussian. Input vectors which are more similar to the prototype return a result closer to 1. Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection The Gaussian Kernel Įach RBF neuron computes a measure of the similarity between the input and its prototype vector (taken from the training set). ![]()
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