Simple Perceptrons. Or one-layer feed-forward networks. Perceptrons or Layered Feed-Forward Networks. Equation governing comp of simple perceptron. activation function, usually nonlinear, e.g. step function or sigmoid. ksi. Threshold or no threshold?. with threshold.
Or one-layer feed-forward networks
activation function, usually nonlinear, e.g. step function or sigmoid
without threshold; threshold simulated with connections to an input terminal permanently tied to -1
Is to ask for: actual output pattern = target pattern
Weights and each input pattern live in the same space.
Advantage: can geometrically represent these two vectors together.
Simplified Simple Learning Algorithm interpretation(for one neuron case)
Weight Update Formula, interpretation“Hebbian” from blue book, too complicated