“Sudden” transition to perfect generalization in binary perceptrons is investigated. Building on recent theoretical works of Gardner and Derrida (1989) and Baum and Lyuu (1991), we show the following: for α > αc = 1.44797 …, if α n examples are drawn from the uniform distribution on {+1, −1}n and classified according to a target perceptron wt ∈ {+1, −1}n as positive if wt · x ≥ 0 and negative otherwise, then the expected number of nontarget perceptrons consistent with the examples is 2−⊖(√n); the same number, however, grows exponentially 2⊖(n) if α ≤ αc. Numerical calculations for n up to 1,000,002 are reported.

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