Backpropagation separates when perceptrons do

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Abstract
Consideration is given to the behavior of the least-squares problem that arises when one attempts to train a feedforward net with no hidden neurons. It is assumed that the net has monotonic nonlinear output units. Under the assumption that a training set is separable, that is, that there is a set of achievable outputs for which the error is zero, the authors show that there are no nonglobal minima. More precisely, they assume that the error is of a threshold least-mean square (LMS) type, in that the error function is zero for values beyond the target value. The authors' proof gives, in addition, the following stronger result: the continuous gradient adjustment procedure is such that from any initial weight configuration a separating set of weights is obtained in finite time. Thus they have a precise analog of the perceptron learning theorem. The authors contrast their results with the more classical pattern recognition problem of threshold LMS with linear output units.

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