Abstract
In this paper we show how neural networks can be formulated in terms of various parameterised connection models which explicitly encode desired properties of the target system. Such a modelling approach to neural networks raises issues about their relationships to other technologies such as Adaptive Filtering and Principal Components Analysis. The benefits of this approach can be a significant decrease in the parameter space, improved generalisation, and a learning procedure which guarantees a priori specified invariance constraints.
Original language | English |
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Pages (from-to) | 613-625 |
Number of pages | 13 |
Journal | Neural Networks |
Volume | 6 |
Issue number | 5 |
DOIs | |
Publication status | Published - 1 Jan 1993 |
Externally published | Yes |
Keywords
- Adaptive filters
- Classification
- Invariance
- Number of parameters