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NeuroCOLT |
Neural Networks and Computational Learning Theory |
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NeuroCOLT Technical Report NC-TR-95-049
Generalisation of A Class of Continuous Neural Networks John
Shawe-Taylor and Jieyu Zhao Abstract We propose a way of using boolean circuits to perform real valued computation in a way that naturally extends their boolean functionality. The functionality of multiple fan in threshold gates in this model is shown to mimic that of a hardware implementation of continuous Neural Networks. A Vapnik-Chervonenkis dimension and sample size analysis for the systems is performed giving best known sample sizes for a real valued Neural Network. Experimental results confirm the conclusion that the sample sizes required for the networks are significantly smaller than for sigmoidal networks.
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