Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Generalization by weight-elimination with application to forecasting
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Adaptive mixtures of local experts
Neural Computation
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Expression Inference is a parsimonious, comprehensible alternative to semi-parametric andnon-parametric classification techniques such as neural networks, which generates compact symbolic mathematical expressions for classification or regression. This paper introduces a general framework for inferring symbolic classifiers, using the Genetic Programming paradigm with non-linear optimisation of embedded coefficients. An error propagation algorithm is introduced to support the optimisation. A multiobjective variant of Genetic Programming provides a range of models trading off parsimony and classification performance, the latter measuredb y ROC curve analysis. The technique is shown to develop extremely concise and effective models on a sample real-world problem domain.