Title :
A fast OBS pruning algorithm based on pseudo-entropy of weights
Author :
Zhao, Shouling ; Liu, Quan ; Zhang, Binbin
Author_Institution :
Sch. of Comput. Sci. & Technol., Soochow Univ., Suzhou, China
Abstract :
A fast OBS pruning algorithm based on pseudo-entropy of weights is proposed to resolve the problems of the number of hidden neurons is difficult to be determined in neural networks and low pruning speed in conventional OBS (Optimal Brain Surgeon) pruning algorithm. The algorithm makes the network constrain the distribution of weight automatically during the training process, obtain a simpler structure of network, and improve the speed of pruning. The result of experimental shows that the structure of network has been simplified; the generalization capability of network and the speed of pruning have been improved greatly by using the algorithm mentioned above.
Keywords :
backpropagation; entropy; generalisation (artificial intelligence); neural nets; OBS pruning algorithm; generalization capability; neural networks; optimal brain surgeon pruning algorithm; weight pseudoentropy; Biological neural networks; Computer networks; Computer science; Entropy; Feeds; Information theory; Network topology; Neurons; Optimization methods; Surges; BP neural network; OBS pruning algorithm; generalization capability; pseudo-entropy of weights;
Conference_Titel :
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4244-5845-5
DOI :
10.1109/ICACC.2010.5486816