- author = "Alan J. Barton and Julio J. Valdes and Robert Orchard",
- title = "Learning the neuron functions within a neural network via Genetic Programming: Applications to geophysics and hydrogeology",
- booktitle = "International Joint Conference on Neural Networks, IJCNN 2009",
- year = "2009",
- pages = "264--271",
- address = "Atlanta, Georgia, USA",
- month = jun # " 14-19",
- keywords = "genetic algorithms, genetic programming, gene expression programming, geophysics, geophysics computing, hydrology, neural nets, geophysics, hydrogeology, neural network classifier, neural network neurons, neuron functions",
-
DOI = "
doi:10.1109/IJCNN.2009.5178731",
- size = "8 pages",
- abstract = "A neural network classifier is sought. Classical neural network neurons are aggregations of a weight multiplied by an input value and then controlled via an activation function. This paper learns everything within the neuron using a variant of genetic programming called gene expression programming. That is, this paper does not explicitly use weights or activation functions within a neuron, nor bias nodes within a layer. Promising preliminary results are reported for a study of the detection of underground caves (a 1 class problem) and for a study of the interaction of water and minerals near a glacier in the Arctic (a 5 class problem).",
-
notes = "
one class membership. ANN variable with 0 mean 1 standard deviation.
Also known as \cite{5178731} See \cite{Barton2009614}",
