Johnson, Martin and Allinson, Nigel (1989) Implementation of a variable cluster self organising algorithm for high speed unsupervised pattern classification (lost in {0, 1}N space). In: Automated Inspection and High-Speed Vision Architectures III, 6-7 November 1989, Philadelphia, PA, USA.
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| Item Type: | Conference or Workshop contribution (Paper) |
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| Item Status: | Live Archive |
Abstract
Traditional neural networks for pattern classification use linear decisions to partition a multivalued high dimensional pattern space. This paper shows that the properties of binary space ({0, 1}N space) make it well suited for these tasks and a simple training algorithm is given. A simple measure of network ordering is used to allow a variable number of clusters and continuous learning.
| Keywords: | Image Processing--Reconstruction, Neural Networks, Binary Space, Grey Scale Images, Kohonen Algorithm, Pattern Classification, Self-Organizing Algorithm, Training Algorithm, Image Storage, Digital |
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| Subjects: | G Mathematical and Computer Sciences > G400 Computer Science G Mathematical and Computer Sciences > G730 Neural Computing |
| Divisions: | College of Science > School of Computer Science |
| Related URLs: | |
| ID Code: | 8629 |
| Deposited On: | 31 May 2013 08:34 |
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