explain de centraliazatio n in neural networks with Signum function method of discrimination?? I need correct...
explain de centraliazatio n in neural networks with Signum function method of discrimination?? I need correct explanation and will be rated only for that
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The signum function representation in neural networks is used to adapt the neural networks to solve classification problems. In regression problems, the function to be used is a continuous-valued function of the input variables. In classification problems, the output has to be a 1 or 0. This is achieved by "firing of the neuron". This firing is done with the sigmoid function. The sigmoid function is defined as below:
Thus, when z is very small, g(z) is 0, and when z is very large, g(z) is 1. When z = 0, g(z) = 0.5. Thus, a neural network for an OR circuit can be achieved with the sigmoid function as below:
Note how the sigmoid function converts z into g(z), which is a binary output.
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explain de centraliazatio n in neural networks with
Signum function method of discrimination?? I need correct...
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In more details, the
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