An exploration of a new paradigm for weightless RAM-based neural networks
This paper introduces a novel networking strategy for RAM-based neurons which significantly improves the training and recognition performance of such networks whilst maintaining the generalization capabilities achieved in previous network configurations. A number of different architectures are introduced, each using the same underlying principles. Initially, features which are common to all architectures are described, illustrating the basis of the underlying paradigm. Three architectures are then introduced illustrating different techniques of employing the paradigm to meet differing performance specifications. The architectures are described in terms of the structure of the neurons they employ. Details of the various training and recognition algorithms employed by the architectures are supplied in order to present a complete description of the operation of this class of artificial neural network.
Keywords: FLEXIBLE ARCHITECTURE; ONE-SHOT LEARNING; WEIGHTLESS NETWORKS
Document Type: Research Article
Publication date: 01 March 2000
- Editorial Board
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content