Hybrid Algorithm for the Optimization of Training Convolutional Neural Network
| Autor: | Tony Han, Naz E. Islam, Hayder M. Albeahdili |
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| Rok vydání: | 2015 |
| Předmět: |
Meta-optimization
General Computer Science Computer science business.industry Particle swarm optimization Machine learning computer.software_genre Hybrid algorithm Convolutional neural network Genetic algorithm Benchmark (computing) Artificial intelligence Multi-swarm optimization business computer MNIST database |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 6 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2015.061011 |
| Popis: | The training optimization processes and efficient fast classification are vital elements in the development of a convolution neural network (CNN). Although stochastic gradient descend (SGD) is a Prevalence algorithm used by many researchers for the optimization of training CNNs, it has vast limitations. In this paper, it is endeavor to diminish and tackle drawbacks inherited from SGD by proposing an alternate algorithm for CNN training optimization. A hybrid of genetic algorithm (GA) and particle swarm optimization (PSO) is deployed in this work. In addition to SGD, PSO and genetic algorithm (PSO-GA) are also incorporated as a combined and efficient mechanism in achieving non trivial solutions. The proposed unified method achieves state-of-the-art classification results on the different challenge benchmark datasets such as MNIST, CIFAR-10, and SVHN. Experimental results showed that the results outperform and achieve superior results to most contemporary approaches. |
| Databáze: | OpenAIRE |
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