LEARNING WITH A TEMPERATURE-DEPENDENT ALGORITHM

We analyse the properties of a new learning algorithm for binary perce ptrons based on the minimization of a temperature-dependent differenti able cost function. We show that learning at finite temperature increa ses the stabilities of learned patterns, endowing the perceptron with robustness, at the price of accepting a small fraction of errors in th e learning set. If the temperature is appropriately chosen, our algori thm approaches the optimal generalization performance for linearly sep arable functions. Therefore, by controlling the learning temperature, this algorithm solves the main practical problem of perceptron learnin g, i.e. that of finding the best weights, independently of the nature of the learning set.