Name
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. |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1109/34.476519 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
mathematical analysis,similar existing classifier,boolean k-nearest neighbor,continuous feature,supervised classifiers,supervised pattern,hardware implementation,well-known binary,boolean neural network,boolean neural networks,neural networks,feedforward neural networks,handwriting recognition,k nearest neighbor,system testing,pattern recognition,learning artificial intelligence,art,pattern analysis,hardware,boolean algebra,artificial neural networks,neural network | Pattern recognition,Computer science,Robustness (computer science),Time delay neural network,Feature (machine learning),Boolean algebra,Artificial intelligence,Deep learning,Artificial neural network,Classifier (linguistics),Machine learning,Binary number | Journal |
Volume | Issue | ISSN |
17 | 12 | 0162-8828 |
Citations | PageRank | References |
16 | 1.69 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Srinivas Gazula | 1 | 16 | 1.69 |
| Mansur R. Kabuka | 2 | 250 | 16.95 |