Advances in neural information processing systems 2
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Convolutional networks for images, speech, and time series
The handbook of brain theory and neural networks
Adaptive mixtures of local experts
Neural Computation
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It is widely believed in the pattern recognition field that the number of examples needed to achieve an acceptable level of generalization ability depends on the number of independent parameters needed to specify the network configuration. The paper presents a neural network for classification of highdimensional patterns. The network architecture proposed here uses a layer which extracts the global features of patterns. The layer contains neurons whose weights are induced by a neural subnetwork. The method reduces the number of independent parameters describing the layer to the parameters describing the inducing subnetwork.