Setiono R
Department of Information Systems and Computer Science, National University of Singapore, Kent Ridge, Republic of Singapore.
Neural Comput. 1997 Jan 1;9(1):185-204. doi: 10.1162/neco.1997.9.1.185.
This article proposes the use of a penalty function for pruning feedforward neural network by weight elimination. The penalty function proposed consists of two terms. The first term is to discourage the use of unnecessary connections, and the second term is to prevent the weights of the connections from taking excessively large values. Simple criteria for eliminating weights from the network are also given. The effectiveness of this penalty function is tested on three well-known problems: the contiguity problem, the parity problems, and the monks problems. The resulting pruned networks obtained for many of these problems have fewer connections than previously reported in the literature.
本文提出通过权重消除使用惩罚函数来修剪前馈神经网络。所提出的惩罚函数由两项组成。第一项是抑制使用不必要的连接，第二项是防止连接的权重取值过大。还给出了从网络中消除权重的简单标准。在三个著名问题上测试了该惩罚函数的有效性：邻接问题、奇偶问题和僧侣问题。针对其中许多问题得到的修剪后的网络比文献中先前报道的连接更少。
