Training a perceptron in a discrete weight space
On-line and batch learning of a perceptron in a discrete weight space, where each weight can take $2 L+1$ different values, are examined analytically and numerically. The learning algorithm is based on the training of the continuous perceptron and prediction following the clipped weights. The learning is described by a new set of order parameters, composed of the overlaps between the teacher and the continuous/clipped students. Different scenarios are examined among them on-line learning with discrete/continuous transfer functions and off-line Hebb learning. The generalization error of the clipped weights decays asymptotically as $exp(-K α^2)$/$exp(-e^{|λ| α})$ in the case of on-line learning with binary/continuous activation functions, respectively, where $α$ is the number of examples divided by N, the size of the input vector and $K$ is a positive constant that decays linearly with 1/L. For finite $N$ and $L$, a perfect agreement between the discrete student and the teacher is obtained for $α\propto \sqrt{L \ln(NL)}$. A crossover to the generalization error $\propto 1/α$, characterized continuous weights with binary output, is obtained for synaptic depth $L > O(\sqrt{N})$.