Deep evolving GMDH-SVM-neural network and its learning for Data Mining tasks

@article{Setlak2016DeepEG,
  title={Deep evolving GMDH-SVM-neural network and its learning for Data Mining tasks},
  author={Galina Setlak and Yevgeniy V. Bodyanskiy and Olena Vynokurova and Iryna Pliss},
  journal={2016 Federated Conference on Computer Science and Information Systems (FedCSIS)},
  year={2016},
  pages={141-145}
}
In the paper, the deep evolving neural network and its learning algorithms (in batch and on-line mode) are proposed. The deep evolving neural network's architecture is developed based on GMDH approach (in J. Schmidhuber's opinion it is historically first system, which realizes deep learning ) and least squares support vector machines with fixed number of the synaptic weights, which provide high quality of approximation in addition to the simlicity of implementation of nodes with two inputs. The… 
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