LEARNING AND GENERALIZATION WITH MINIMERROR, A TEMPERATURE-DEPENDENT LEARNING ALGORITHM

We study the numerical performances of Minimerror, a recently introduc ed learning algorithm for the perceptron that has analytically been sh own to be optimal both on learning linearly and nonlinearly separable functions. We present its implementation on learning linearly separabl e boolean functions. Numerical results are in excellent agreement with the theoretical predictions.