Titlebook: Combining Artificial Neural Nets; Ensemble and Modular Amanda J. C. Sharkey Book 1999 Springer-Verlag London Limited 1999 Ensembl.cognition

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1431-6854 ifferent nets trained on the same task; the modular approachThe past decade could be seen as the heyday of neurocomputing: in which the capabilities of monolithic nets have been well explored and exploited. The question then is where do we go from here? A logical next step is to examine the potentia

parallelism

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Self-Organised Modular Neural Networks for Encoding Data,-dimensional data space is broken up into a number of lowdimensional subspaces, each of which is separately encoded. This type of factorial encoder emerges through a process of self-organisation, provided that the input data lies on a curved manifold, as is indeed the case in image processing applications.

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Model Selection of Combined Neural Nets for Speech Recognition,ask and in digit recognition over a noisy telephone line. Bootstrap estimates of minimum MSE allow selection of regression models that improve system recognition performance. The procedure allows a flexible strategy for dealing with inter-speaker variability without requiring an additional validatio

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1431-6854 ntages of modular design and reuse advocated by object-oriented programmers. And it is not surprising to find that the same principles can be usefully applied in the field of neurocomput­ ing as well, although finding the best way of adapting them is a subject of on-going research.978-1-85233-004-0978-1-4471-0793-4Series ISSN 1431-6854