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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Features Combined From Hundreds of Midlayers: Hierarchical Networks With Subnetwork Nodes
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Features Combined From Hundreds of Midlayers: Hierarchical Networks With Subnetwork Nodes

机译：数百个中间层相结合的功能：具有子网节点的分层网络

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摘要

In this paper, we believe that the mixed selectivity of neuron in the top layer encodes distributed information produced from other neurons to offer a significant computational advantage over recognition accuracy. Thus, this paper proposes a hierarchical network framework that the learning behaviors of features combined from hundreds of midlayers. First, a subnetwork neuron, which itself could be constructed by other nodes, is functional as a subspace features extractor. The top layer of a hierarchical network needs subspace features produced by the subnetwork neurons to get rid of factors that are not relevant, but at the same time, to recast the subspace features into a mapping space so that the hierarchical network can be processed to generate more reliable cognition. Second, this paper shows that with noniterative learning strategy, the proposed method has a wider and shallower structure, providing a significant role in generalization performance improvements. Hence, compared with other state-of-the-art methods, multiple channel features with the proposed method could provide a comparable or even better performance, which dramatically boosts the learning speed. Our experimental results show that our platform can provide a much better generalization performance than 55 other state-of-the-art methods.
机译：在本文中，我们认为顶层中神经元的混合选择性编码了其他神经元产生的分布式信息，从而提供了优于识别精度的显着计算优势。因此，本文提出了一个分层的网络框架，该框架将特征的学习行为与数百个中间层组合在一起。首先，本身可以由其他节点构造的子网神经元充当子空间特征提取器。分层网络的顶层需要由子网络神经元产生的子空间特征，以消除不相关的因素，但同时将子空间特征重铸到映射空间中，以便可以处理该分层网络以生成更可靠的认知。其次，本文表明，采用非迭代学习策略，该方法具有较宽和较浅的结构，在提高泛化性能方面发挥了重要作用。因此，与其他最新方法相比，所提出的方法的多通道功能可以提供可比甚至更好的性能，从而极大地提高了学习速度。我们的实验结果表明，我们的平台可以提供比55种其他最新技术更好的泛化性能。

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