A fast learning algorithm for parismonious fuzzy neural systems
Fuzzy Sets and Systems - Information processing
A method of face recognition based on fuzzy clustering and parallel neural networks
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
IEEE Transactions on Neural Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Applying falsity input to neural networks to solve single output regression problems
ACACOS'11 Proceedings of the 10th WSEAS international conference on Applied computer and applied computational science
The SAR image compression with projection pursuit neural networks
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Structural design of optimized polynomial radial basis function neural networks
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Hi-index | 0.00 |
We introduce a statistically based methodology for the design of neural networks when the dimension d of the network input is comparable to the size n of the training set. If one proceeds straightforwardly, then one is committed to a network of complexity exceeding n. The result will be good performance on the training set but poor generalization performance when the network is presented with new data. To avoid this we need to select carefully the network architecture, including control over the input variables. Our approach to selecting a network architecture first selects a subset of input variables (features) using the nonparametric statistical process of difference-based variance estimation and then selects a simple network architecture using projection pursuit regression (PPR) ideas combined with the statistical idea of slicing inverse regression (SIR). The resulting network, which is then retrained without regard to the PPR/SIR determined parameters, is one of moderate complexity (number of parameters significantly less than n) whose performance on the training set can be expected to generalize well. The application of this methodology is illustrated in detail in the context of short-term forecasting of the demand for electric power from an electric utility