A Polynomial Time Algorithm for the Construction and Training of a Class of Multi-Layer Perceptrons

该奖项资助了一类多层感知器的研究- 比如神经网络解决分类问题。 该算法 使用线性规划方法增量生成隐藏的 层在一个受限制的高阶感知，这是 可以在多项式时间内训练。 该研究将调查 算法的扩展以解决分类问题 对于大型训练集，通常需要很长时间来 收敛。 算法的行为将在 计算机视觉和机器人的典型问题。

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.