Local minima and attractors at infinity for gradient descent learning algorithms
In the paper Learning Nonlinearly Parametrized Decision Re gions an online scheme for learning a very general class of decision regions is given together with conditions on both the parametriza tion and on the sequence of input examples under which good learn ing can be guaranteed to occur In this paper we discuss these condi tions in particular the requirement that there be no non global local minima of the relevant error function and the more speci c problem of no attractor at in nity Somewhat simpler su cient conditions are given A number of examples are discussed