Implementation of a variable cluster self organising algorithm for high speed unsupervised pattern classification (lost in {0, 1}N space)

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)
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
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
ID Code:8629
Deposited On:31 May 2013 08:34

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