By Tebelskis J.
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Additional resources for Speech Recognition using Neural Networks
Example text
3. Computation Computation always begins by presenting an input pattern to the network, or clamping a pattern of activation on the input units. Then the activations of all of the remaining units are computed, either synchronously (all at once in a parallel system) or asynchronously (one at a time, in either randomized or natural order), as the case may be. In unstructured networks, this process is called spreading activation; in layered networks, it is called forward propagation, as it progresses from the input layer to the output layer.
5. In the first case, xj is the dot product between an input vector y and a weight vector w, so xj is the length of the projection of y onto w, as shown in panel (a). , it may lie either on one side or the other of a hyperplane that is perpendicular to w. Inputs that lie on the same side will have xj > 0, while inputs that lie on the opposite side will have xj < 0. Thus, if y j = f ( x j ) is a threshold function, as in Equation (24), then the unit will classify each input in terms of which side of the hyperplane it lies on.
For example, in a Radial Basis Function network (Moody and Darken 1989), the hidden layer contains units that describe hyperspheres (trained with a standard competitive learning algorithm), while the output layer computes normalized linear combinations of these receptive field functions (trained with the Delta Rule). The attraction of such hybrid networks is that they reduce the multilayer backpropagation algorithm to the single-layer Delta Rule, considerably reducing training time. On the other hand, since such networks are trained in terms of independent modules rather than as an integrated whole, they have somewhat less accuracy than networks trained entirely with backpropagation.