Human Face Recognition
Learning Algorithm (Backpropagation)
Learning process in Backpropagation requires providing pairs of input and target vectors.
The output vector o of each input vector is compared with target vector t. In case of
difference the weights are adjusted to minimize the difference. Initially random weights and thresholds are assigned to the network. These weights are updated every iteration in order to minimize the cost function or the mean square error between the output vector and the target vector.
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