Evolutionary product unit based neural networks for regression
Neural Networks
Computational techniques for spatial logistic regression with large data sets
Computational Statistics & Data Analysis
International Journal of Approximate Reasoning
Engineering Applications of Artificial Intelligence
Fractal projection pursuit classification model applied to geochemical survey data
Computers & Geosciences
Pattern analysis by active learning method classifier
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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We study and compare two types of connectionist learning methods for model-free regression problems: 1) the backpropagation learning (BPL); and 2) the projection pursuit learning (PPL) emerged in recent years in the statistical estimation literature. Both the BPL and the PPL are based on projections of the data in directions determined from interconnection weights. However, unlike the use of fixed nonlinear activations (usually sigmoidal) for the hidden neurons in BPL, the PPL systematically approximates the unknown nonlinear activations. Moreover, the BPL estimates all the weights simultaneously at each iteration, while the PPL estimates the weights cyclically (neuron-by-neuron and layer-by-layer) at each iteration. Although the BPL and the PPL have comparable training speed when based on a Gauss-Newton optimization algorithm, the PPL proves more parsimonious in that the PPL requires a fewer hidden neurons to approximate the true function. To further improve the statistical performance of the PPL, an orthogonal polynomial approximation is used in place of the supersmoother method originally proposed for nonlinear activation approximation in the PPL