Machine Learning - Special issue on context sensitivity and concept drift
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
Exploiting Context When Learning to Classify
ECML '93 Proceedings of the European Conference on Machine Learning
Context-dependent neural nets-structures and learning
IEEE Transactions on Neural Networks
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In the paper, we present the model of a context-dependent neural net - a net which may change the way it works according to the external conditions. The information about the environmental conditions is fed to the net through the context inputs, which are used to calculate the net's weights, and as a consequence modify the way the net reacts to the traditional inputs. We discuss the Vapnik-Chervonenkis dimension of such a neuron and show that the separating power of a context-dependent neuron and multilayer net grows with the number of adjustable parameters. We present the difference in the way traditional and context-dependent nets work and compare the input space transformations both of them are able to perform. We also show that context-dependent nets learn faster than traditional ones with the same VC-dimension.