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Features Combined From Hundreds of Midlayers: Hierarchical Networks With Subnetwork Nodes.
- Source :
- IEEE Transactions on Neural Networks & Learning Systems; Nov2019, Vol. 30 Issue 11, p3313-3325, 13p
- Publication Year :
- 2019
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- LEARNING strategies
ARTIFICIAL neural networks
ITERATIVE learning control
Subjects
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 30
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 139436788
- Full Text :
- https://doi.org/10.1109/TNNLS.2018.2890787