神经网络的自适应删剪学习算法及其应用

ADAPTIVE TRAINING AND PRUNING FOR NEURAL NETWORKS: ALGORITHMS AND APPLICATION

在局域卡尔曼滤波算法的基础上 ,提出了一种自适应删剪学习算法 ,这一算法的核心是用网络训练结束后得到的局域的误差协方差矩阵测量权重的重要性 ,通过删除不重要的权重 ,得到一个紧凑的网络结构 .

Finding an optimal network size is one of the major concerns when building a neural network. In using the local extended Kalman filter (EKF) algorithm, we propose an efficient approach that combines EKF training and pruning as a whole. In particular, the covariance matrix obtained along with the local EKF training can be utilized to indicate the importance of the network weights. As a result, the network size can be determined adaptively to keep pace with the changes in input characteristics. The effectiveness of this algorithm is demonstrated on generalized XOR logic function and handwritten digit recognition.