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Perceptron Learning (2)

  • The weight updates need to be applied repeatedly for each weight Wj in the network, and for each training suite in the training set.
  • One such cycle through all weighty is called an epoch of training.
  • Eventually, mostly after many epochs, the weight changes converge towards zero and the training process terminates.
  • The perceptron learning process always finds a set of weights for a perceptron that solves a problem correctly with a finite number of epochs, if such a set of weights exists.
  • If a problem can be solved with a separation hyperplane, then the set of weights is found in finite iterations and solves the problem correctly.

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