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文章基本信息

  • 标题:Visualization analysis of feed forward neural network input contribution
  • 本地全文:下载
  • 作者:Jamal Alsakran ; Ali Rodan ; Nouh Alhindawi
  • 期刊名称:Scientific Research and Essays
  • 印刷版ISSN:1992-2248
  • 出版年度:2014
  • 卷号:9
  • 期号:14
  • 页码:645-651
  • DOI:10.5897/SRE2014.5895
  • 语种:English
  • 出版社:Academic Journals
  • 摘要:The complexity of domain problem can slow or even hinder the learning process of neural networks. It is rather difficult to overcome such an obstacle because neural networks, as cited today in the literature, lack the interpretability of their internal structures. In this paper, we present a visualization approach capable of enhancing the understanding of neural networks. Our approach visualizes input and weight contributions, sensitivity analysis, and provides guidance in pruning less influential features and consequently reducing the complexity of domain problem while maintaining acceptable error rates. We conduct experiments on various datasets to show the effectiveness of our approach.
  • 关键词:Neural network; visualization; input contribution; sensitivity analysis " />
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