Treating Artificial Neural Net Training as a Nonsmooth Global Optimization Problem

摘要

We attack the classical neural network training problem by successive piecewise linearization, applying three different methods for the global optimization of the local piecewise linear models. The methods are compared to each other and steepest descent as well as stochastic gradient on the regression problem for the Griewank function.