Theoretical Foundations of Neural Networks

C. M. Bishop

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Neural networks have often been motivated by superficial analogy with biological nervous systems. Recently, however, it has become widely recognised that the effective application of neural networks requires instead a deeper understanding of the theoretical foundations of these models. Insight into neural networks comes from a number of fields including statistical pattern recognition, computational learning theory, statistics, information geometry and statistical mechanics. As an illustration of the importance of understanding the theoretical basis for neural network models, we consider their application to the solution of multi-valued inverse problems. We show how a naive application of the standard least-squares approach can lead to very poor results, and how an appreciation of the underlying statistical goals of the modelling process allows the development of a more general and more powerful formalism which can tackle the problem of multi-modality.

Original languageEnglish
Title of host publicationProceedings of Physics Computing 96, Krakow
PublisherAcademic Computer Centre
Pages500-507
Number of pages8
Publication statusPublished - 1 Jan 1996

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