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Design of Supervised Classifiers Using Boolean Neural Networks
December 1995 (vol. 17 no. 12)
pp. 1239-1246

Abstract—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.

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Index Terms:
Boolean neural networks, pattern classification, high performance recognition neural networks, pattern analysis, pattern recognition.
Citation:
Srinivas Gazula, Mansur R. Kabuka, "Design of Supervised Classifiers Using Boolean Neural Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 12, pp. 1239-1246, Dec. 1995, doi:10.1109/34.476519
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