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Machine Learning, 2nd Edition by Stephen Marsland

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Chapter 5

Radial Basis Functions and Splines

In the Multi-layer Perceptron, the activations of the hidden nodes were decided by whether the inputs times the weights were above a threshold that made the neuron fire. While we had to sacrifice some of this ideal to the requirement for differentiability, it was still the case that the product of the inputs and the weights was summed, and if it was well above the threshold then the neuron fired, if it was well below the threshold it did not, and between those values it acted linearly. For any input vector several of the neurons could fire, and the outputs of these neurons times their weights were then summed in the second layer to decide which neurons should fire there. This has the result that the ...

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