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An Implementation of Generalized Second-Order Neural Networks for Pattern Recognition  

Lee Bong-Kyu (제주대학교 전산통계학과)
Yang Yo-Han (제주대학교 전산통계학과)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers D / v.51, no.10, 2002 , pp. 446-452 More about this Journal
Abstract
For most of pattern recognition applications, it is required to correctly recognize patterns even if they have translation variations. In this paper, to achieve the goal of translation invariant pattern recognition, we propose a new generalized translation invariant second-order neural network using a constraint on the weights. The weight constraint is implemented using generalized translation invariant features which are accumulated sums of pixel combinations. Simulation results will be given to demonstrate that the proposed second-order neural network has the generalized translation invariant property.
Keywords
translation invariant; generalization; second-order neural network; weight constraint; pixel combination;
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Times Cited By KSCI : 1  (Citation Analysis)
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