• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >The Growing Hierarchical Neural Gas Self-Organizing Neural Network
【24h】

The Growing Hierarchical Neural Gas Self-Organizing Neural Network

机译：日益增长的分层神经气体自组织神经网络

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The growing neural gas (GNG) self-organizing neural network stands as one of the most successful examples of unsupervised learning of a graph of processing units. Despite its success, little attention has been devoted to its extension to a hierarchical model, unlike other models such as the self-organizing map, which has many hierarchical versions. Here, a hierarchical GNG is presented, which is designed to learn a tree of graphs. Moreover, the original GNG algorithm is improved by a distinction between a growth phase where more units are added until no significant improvement in the quantization error is obtained, and a convergence phase where no unit creation is allowed. This means that a principled mechanism is established to control the growth of the structure. Experiments are reported, which demonstrate the self-organization and hierarchy learning abilities of our approach and its performance for vector quantization applications.
机译：不断增长的神经气体（GNG）自组织神经网络是无监督学习处理单元图的最成功示例之一。尽管它取得了成功，但它对扩展到分层模型的关注却很少，与其他模型（例如具有许多分层版本的自组织图）不同。在这里，提出了一个分层的GNG，旨在学习图的树。此外，原始GNG算法通过以下区别得到改进：在增长阶段添加了更多的单元，直到没有获得量化误差的显着改善为止；在收敛阶段，不允许创建任何单元。这意味着建立了一种原理性的机制来控制结构的增长。报告了实验，这些实验证明了我们方法的自组织和层次学习能力以及其在矢量量化应用中的性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号