Presentation 1994/5/19
Increase the Capability of MLP Using Cross-Layer Connections
Qiangfu Zhao,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) In the study of multilayer perceptrons(MLP),a common assumption by all researchers is that no cross-layer connections exist in the network.Although learning of MLP under this assumption becomes very simple,the capability of MLP is also limited.For highly nonlinear decision problems,if the number of neurons in a certain layer is not large enough,samples of different patterns may be mapped into one point in the next layer,and this error can not be corrected in the following layers regardless how many layers we use.This problem however,can be solved simply by using cross-layer connections.In the MLP with cross-layer connections(CLC-MLP),part of the data used in a lower layer can also be used in the higher layers,and classification errors made in a layer can be corrected in the higher layers by using these data appropriately.In this paper,the structure,the neuron models and the learning rules for CLC-MLP are studied,and several new ideas are proposed.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Multi-Layer Perceptron / Pattern Recognition / Competitive Learing / Self-Organization / Cross-Layer Connections
Paper # NC94-8
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Conference Information
Committee NC
Conference Date 1994/5/19(1days)
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Paper Information
Registration To Neurocomputing (NC)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Increase the Capability of MLP Using Cross-Layer Connections
Sub Title (in English)
Keyword(1) Multi-Layer Perceptron
Keyword(2) Pattern Recognition
Keyword(3) Competitive Learing
Keyword(4) Self-Organization
Keyword(5) Cross-Layer Connections
1st Author's Name Qiangfu Zhao
1st Author's Affiliation Graduate School of Information Sciences,Tohoku University()
Date 1994/5/19
Paper # NC94-8
Volume (vol) vol.94
Number (no) 40
Page pp.pp.-
#Pages 6
Date of Issue