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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1245

Title: Region-mapping neural network model for pattern recognition
Authors: Li, Yanlai
Wang, Kuanquan
Zhang, David D.
Subjects: Region-mapping model
Supervisor region
Neural network
Pattern recognition
Issue Date: 2002
Publisher: IEEE
Citation: 1st International Conference on Machine Learning and Cybernetics : November 4-5, 2002, Beijing, China : proceedings, v. 3, p. 1541-1545.
Abstract: In general, the process for multilayer feedforward neural network in pattern recognition is composed of two phases: training and classifying. The aim of the training phase is to make the network output meet the desired output given by the training patterns as possible. It demands a map of point to point, which is so strict that it often causes the criterion inconsistence between training and classifying. Consequently the recognition rate would be decreased. Region-mapping model has changed the output space from one point to a certain supervisor region so that it has overcome the shortcoming of inconsistent problem between training and testing as common multilayer perceptron (MLP) does. Furthermore, it has saved much computing time by mapping the input data to an output area rather than an output point. This paper presents a Region-mapping model with quarter hyper globe as supervisor region. The gradient decent algorithm is applied to this model. In order to illustrate the effect of our propounded model, a hand-written letter recognition problem is put into experiment. Moment invariant features are used as input parameters. The simulation results show that the region-mapping model has much better characteristics than those common multiplayer perceptrons. Also, the quarter hyper globe rule is more reasonable than the hypercube one.
Rights: © 2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Conference Paper
URI: http://hdl.handle.net/10397/1245
ISBN: 0780375084
Appears in Collections:COMP Conference Papers & Presentations

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