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Algorithm for ANN Learning

  • Learning is based on training data, and aims at appropriate weights for the perceptrons in a network.

  • Direct computation is in the general case not feasible.

  • An initial random assignment of weights simplifies the learning process that becomes an iterative adjustment process.

  • In the case of single perceptrons, learning becomes the process of moving hyperplanes around; parametrized over time t: Wi(t+1) = Wi(t) + Δ Wi(t)

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