The Hebb Rule for Learning Linearly Separable Boolean Functions: Learning and Generalization
F. Vallet · Europhysics Letters (EPL) · 1989
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.