Отправить статью по E-mail A COMPREHENSIVE EVOLUTIONARY APPROACH FOR NEURAL NETWORK ENSEMBLES AUTOMATIC DESIGN
- Авторы: Bukhtoyarov V.V.1, Semenkin E.S.1
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Учреждения:
- Выпуск: Том 11, № 7 (2010)
- Страницы: 14-19
- Раздел: Статьи
- URL: https://journals.eco-vector.com/2712-8970/article/view/505624
- ID: 505624
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Аннотация
A new comprehensive approach for neural network ensembles design is proposed. It consists of a method of neural networks automatic design and a method of automatic formation of an ensemble solution on the basis of separate neural networks solutions. It is demonstrated that the proposed approach is not less effective than a number of other approaches for neural network ensembles design.
Полный текст
At the present time data analysis systems which are based on intelligent information technologies are increasingly demanded in many fields of human activity and the scale requirements to these systems are continuously increasing. In connection with these facts the problem of developing methods for automatic design and adaptation of IIT for specific tasks is becoming more urgent. Such methods could allow to abandon the use of expensive, mostly human, resources for the design of the IIT and to reduce the time required for the development of intelligent systems. One of the most widely used and popular intellectual technologies are artificial neural networks. The range of problems solved by using neural networks is extremely large because of many advantages of systems based on their use. Despite the fact that this information technology could be called a universal tool for solving problems of data analysis, in each case we have to create a unique neural network. One of the approaches to improve the efficiency of systems based on the use of neural networks is the use of neural networks ensembles. Problem solving with the help of neural network ensembles supposes Vestnik. Scientific Journal of Siberian State Aerospace University named after academician M. F. Reshetnev 15 simultaneous use of a finite number of preliminarily trained neural networks.×
Список литературы
- Hansen L. K., Salamon P. Neural network ensembles // IEEE Trans. Pattern Analysis and Machine Intelligence. 1990. № 12 (10). P. 993–1001.
- Cherkauer K. J. Human expert level performance on a scientific image analysis task by a system using combined artificial neural networks // Proc. AAAI-96 Workshop on Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms : P. Chan, S. Stolfo, D. Wolpert (eds.). Portland, OR : AAAI Press ; Menlo Park, CA. 1996. P. 15–21.
- Hampshire J., Waibel A. A novel objective function for improved phoneme recognition using timedelay neural networks // IEEE Transactions on Neural Networks. 1990. № 1 (2). P. 216–228.
- Goldberg D. E. Genetic algorithms in search, optimization and machine learning. Reading. MA: Addison-Wesley, 1989.
- Semenkin E. S., Sopov E. A. Probabilities-based evolutionary algorithms of complex systems optimization. In Intelligent Systems (AIS’05) and Intelligent CAD (CAD-2005). M. : FIZMATLIT, 2005. Vol. 1. P. 77–79.
- Perrone M. P., Cooper L. N. When networks disagree: ensemble method for neural networks // Artificial Neural Networks for Speech and Vision ; R. J. Mammone (ed.). N. Y. : Chapman - Hall, 1993. P. 126–142.
- Jimenez D. Dynamically weighted ensemble neural networks for classification // Proc. IJCNN-98. Vol. 1. Anchorage: AK. IEEE Computer Society Press; Los Alamitos, CA. 1998. P. 753–756.
- Zhou Z. H., Wu J., Tang W. Ensembling neural networks: Many could be better than all // Artif. Intell. 2002. Vol. 137, № 1–2. P. 239–263.
- Koza J. R. The Genetic Programming Paradigm: Genetically Breeding Populations of Computer Programs to Solve Problems. Cambridge, MA : MIT Press, 1992.
- Yeh I.-C. Modeling slump flow of concrete using second-order regressions and artificial neural networks // Cement and Concrete Composites. 2007. Vol. 29. № 6.P. 474–480.
- URL: http://www.archive.ics.uci.edu/ml/datasetsConcrete_Slump_Test
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