Abstract
Deep learning neural networks have produced some notable well publicized successes in several fields. Genetic Programming has also produced well publicized notable successes. Inspired by the deep learning successes with neural nets, we experiment with deep learning algebra networks where the network remains unchanged but where the neurons are replaced with general algebraic expressions. The training algorithms replace back propagation, counter propagation, etc. with a combination of genetic programming to generate the algebraic expressions and multiple regression, logit regression, and discriminant analysis to train the deep learning algebra network. These enhanced algebra networks are trained on ten theoretical classification problems with good performance advances which show a clear statistical performance improvement as network architecture is expanded.
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Korns, M.F. (2022). Feature Discovery with Deep Learning Algebra Networks. In: Banzhaf, W., Trujillo, L., Winkler, S., Worzel, B. (eds) Genetic Programming Theory and Practice XVIII. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-16-8113-4_6
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DOI: https://doi.org/10.1007/978-981-16-8113-4_6
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