Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Soft competitive adaptation: neural network learning algorithms based on fitting statistical mixtures
Neural Computation
Improving Performance in Neural Networks Using a Boosting Algorithm
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Square Unit Augmented, Radially Extended, Multilayer Perceptrons
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
A Hybrid Projection Based and Radial Basis Function Architecture
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Adaptive mixtures of local experts
Neural Computation
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Forward and Backward Selection in Regression Hybrid Network
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Bias-Variance Analysis and Ensembles of SVM
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
International Journal of Knowledge Engineering and Data Mining
Local additive regression of decision stumps
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
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We introduce an algorithm for incrementaly constructing a hybrid network fo radial and perceptron hidden units. The algorithm determins if a radial or a perceptron unit is required at a given region of input space. Given an error target, the algorithm also determins the number of hidden units. This results in a final architecture which is often much smaller than an RBF network or a MLP. A benchmark on four classification problems and three regression problems is given. The most striking performance improvement is achieved on the vowel data set [4].