Patent ID: 8918352

Claim:
A computer-implemented process comprising: receiving into memory data defining a single hidden layer neural network which includes a linear input layer, a nonlinear hidden layer, and a linear output layer, having lower layer weights applied to the input layer to provide an output of the hidden layer and upper layer weights applied to the output of the hidden layer to provide an output of the output layer; computing a gradient of a square error with respect to the lower layer weights, wherein the gradient of the square error is a first function derived by a. specifying a second function defining the gradient of the square error using the lower layer weights and the upper layer weights, and b. substituting, in the second function, the upper layer weights with a third function defining the upper layer weights using the lower layer weights; and within each learning stage, updating the lower layer weights according to the computed gradient using the first function and then updating the upper layer weights according to the third function defining the upper layer weights using the updated lower layer weights.