# 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…

## 8 Citations

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## References

SHOWING 1-10 OF 36 REFERENCES

Neural Networks and Statistical Learning

- Computer Science
- 2013

Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.

Identification of radial basis function networks by using revised GMDH-type neural networks with a feedback loop

- Computer ScienceProceedings of the 41st SICE Annual Conference. SICE 2002.
- 2002

The revised GMDH-type neural networks with a feedback loop proposed in the paper can identify the radial basis function networks accurately because the complexity of the neural networks is increased gradually by the feedback loop calculations.

Deep learning of support vector machines with class probability output networks

- Computer ScienceNeural Networks
- 2015

Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]

- Computer ScienceIEEE Computational Intelligence Magazine
- 2010

An overview of the mainstream deep learning approaches and research directions proposed over the past decade is provided and some perspective into how it may evolve is presented.

GMDH neural network algorithm using the heuristic self-organization method and its application to the pattern identification problem

- Computer Science, BusinessProceedings of the 37th SICE Annual Conference. International Session Papers
- 1998

The GMDH (group method of data handling) neural network algorithm using the heuristic self-organization method is proposed and the optimal neuron's structures are selected automatically so as to minimize the values of the prediction error criterion AIC and the useless neurons are eliminated from the neural network.

Deep Learning

- Computer ScienceNature
- 2015

Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.

Deep learning in neural networks: An overview

- Computer ScienceNeural Networks
- 2015

Neural Networks and Learning Machines

- Computer Science
- 2010

Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together.

Bronchopulmonary Dysplasia prediction using Support Vector Machine and LIBSVM

- Computer Science2014 Federated Conference on Computer Science and Information Systems
- 2014

The main conclusion is that unlike Matlab SVM[2] implementation, LIBSVM can be successfully used in considered problem, but it is less stable than LR.