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>Training Error Functions</H1
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>Name</H2
>Training Error Functions -- Constants representing errors functions.</DIV
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>Description</H2
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> These constants represent the error functions used when calculating the error during training.
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> The training error function used is chosen by the
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function. The default training error function is <CODE
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>FANN_ERRORFUNC_TANH</CODE
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>FANN_ERRORFUNC_LINEAR</DT
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> The basic linear error function which simply calculates the error as the difference
between the real output and the desired output.
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>FANN_ERRORFUNC_TANH</DT
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> The tanh error function is an error function that makes large deviations
stand out, by altering the error value used when training the network.
The idea behind this is that it is worse to have 1 output that misses the target
by 100%, than having 10 outputs that misses the target by 10%.
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> This is the default error function and it is usually better. It can however
give poor results with high learning rates.
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