Region-mapping neural network model for pattern recognition

Yan Lai Li, Kuan Quan Wang, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of 2002 International Conference on Machine Learning and Cybernetics
Number of pages5
Publication statusPublished - 1 Dec 2002
EventProceedings of 2002 International Conference on Machine Learning and Cybernetics - Beijing, China
Duration: 4 Nov 20025 Nov 2002


ConferenceProceedings of 2002 International Conference on Machine Learning and Cybernetics


  • Neural network
  • Pattern recognition
  • Region-mapping model
  • Supervisor region

ASJC Scopus subject areas

  • Engineering(all)

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