This award funds research on a class of multilayer perceptron- like neural networks for classification problems. The algorithm uses linear programming methods to incrementally generate hidden layers in a restricted higher-order perception, which is trainable in polynomial time. The research will investigate extensions of the algorithm to address classification problems with large training sets, which normally take a long time to converge. Behavior of the algorithm will be evaluated on representative problems from computer vision and robotics.
