Analytical decision boundary feature extraction for neural networks for the recognition of unconstrained handwritten digits

Jinwook Go, Chulhee Lee

Research output: Contribution to journalConference article

3 Citations (Scopus)

Abstract

Although neural networks have been successfully applied for the recognition of unconstrained handwritten characters, there have been few efficient feature extraction algorithms, resulting in inefficient neural networks. In this paper, we apply a recently published decision boundary feature extraction algorithm to neural networks for the recognition of handwritten digits and reduce the computational cost and complexity of neural networks. Experiments show that the proposed feature extraction algorithm can reduce the number of features significantly without sacrificing the performance.

Original languageEnglish
Pages (from-to)2731-2734
Number of pages4
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume4
Publication statusPublished - 2000 Dec 1
Event2000 IEEE International Conference on Systems, Man and Cybernetics - Nashville, TN, USA
Duration: 2000 Oct 82000 Oct 11

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Feature extraction
Neural networks
Costs
Experiments

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Hardware and Architecture

Cite this

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