The Hebb Rule for Learning Linearly Separable Boolean Functions: Learning and Generalization
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We investigate the Hebb solution for the perceptron realization of an arbitrary linearly separable Boolean function defined on the hypercube of dimension N. We calculate the learning and generalization rates in the N → ∞ limit. They can be analytically expressed vs. α = P/N, where P is the number of learned pattern.