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[2021] Surrey - EEEM005: AI and AI Programming - A1.a Perceptron Convergence Theorem

[2021] Surrey - EEEM005: AI and AI Programming - A1.a Perceptron Convergence Theorem

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(a) State the unity-increment weight update formula used in the Perceptron Convergence Theorem for learning a Boolean function. Assume that a target 1 pattern is incorrectly classified if the input to the hard-limiting function is less than or equal to zero. Starting with zero weights, show the first four updates for a Perceptron learning the two-input logic exclusive-OR (XOR) function. Explain why the Perceptron learning XOR will never converge, and give an example of another Boolean function that is not computable by the Perceptron. CourseNana.COM

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