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I have to develop a Radial Basis Function network for handwritten digits classification and have some problem of how many neurons on the output neuron should I use. I have two ideas: -1 10 hidden neurons (one for each digit) and just one output neuron that computes the linear combination (e.g the weight are arranged in a way that the sum outputs the number) -2 use 10 output neurons, one for each digit and the output is a vector with 0s and 1 in the correct position. The number of hidden neurons has to be chose

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