Design of Supervised Classifiers Using Boolean Neural Networks

Srinivas Gazula, Mansur R. Kabuka

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


In this paper we present two supervised pattern classifiers designed using Boolean neural networks (BNN). They are 1) nearest-to-an-exemplar (NTE) and 2) Boolean k-nearest neighbor (BKNN) classifier. The emphasis during the design of these classifiers was on simplicity, robustness, and the ease of hardware implementation. The classifiers use the idea of radius of attraction (ROA) to achieve their goal. Mathematical analysis of the algorithms presented in the paper is done to prove their feasibility. Both classifiers are tested with well-known binary and continuous feature valued data sets yielding results comparable with those obtained by similar existing classifiers.

Original languageEnglish (US)
Pages (from-to)1239-1246
Number of pages8
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number12
StatePublished - Dec 1995


  • Boolean neural networks
  • high performance recognition neural networks
  • pattern analysis
  • pattern classification
  • pattern recognition

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


Dive into the research topics of 'Design of Supervised Classifiers Using Boolean Neural Networks'. Together they form a unique fingerprint.

Cite this