New method to prune the neural network

Weishui Wan*, Kotaro Hirasawa, Jinglu Hu, Chunzhi Jin


研究成果: Paper査読

2 被引用数 (Scopus)


Using backpropagation algorithm (BP) to train neural networks is a widely adopted practice in both theory and practical applications. But its distributed weight representation, that is the weight matrix of final network after training by using BP are usually not sparsified, and prohibits its use in the rule discovery of inherent functional relations between the input and output data, so in this aspect some kinds of structure optimization are needed to improve its poor performance. In this paper with this in mind a new method to prune neural networks is proposed based on some statistical quantities of neural networks. Comparing with the other known pruning methods such as structural learning with forgetting (SLF) and RPROP algorithm, the proposed method can attain comparable or even better results over these methods without evident increase of the computational load. Detailed simulations using the Iris data sets exhibit our above assertion.

出版ステータスPublished - 2000 1月 1
イベントInternational Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy
継続期間: 2000 7月 242000 7月 27


OtherInternational Joint Conference on Neural Networks (IJCNN'2000)
CityComo, Italy

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能


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