Training neural networks with ant colony optimization algorithms for pattern classification
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| 008 | 210128e20150601xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00500-014-1334-5 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00500-014-1334-5 | ||
| 245 | 0 | 0 | |a Training neural networks with ant colony optimization algorithms for pattern classification |h [Elektronische Daten] |c [Michalis Mavrovouniotis, Shengxiang Yang] |
| 520 | 3 | |a Feed-forward neural networks are commonly used for pattern classification. The classification accuracy of feed-forward neural networks depends on the configuration selected and the training process. Once the architecture of the network is decided, training algorithms, usually gradient descent techniques, are used to determine the connection weights of the feed-forward neural network. However, gradient descent techniques often get trapped in local optima of the search landscape. To address this issue, an ant colony optimization (ACO) algorithm is applied to train feed-forward neural networks for pattern classification in this paper. In addition, the ACO training algorithm is hybridized with gradient descent training. Both standalone and hybrid ACO training algorithms are evaluated on several benchmark pattern classification problems, and compared with other swarm intelligence, evolutionary and traditional training algorithms. The experimental results show the efficiency of the proposed ACO training algorithms for feed-forward neural networks for pattern classification. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a Neural networks |2 nationallicence | |
| 690 | 7 | |a Pattern classification |2 nationallicence | |
| 690 | 7 | |a Ant colony optimization |2 nationallicence | |
| 700 | 1 | |a Mavrovouniotis |D Michalis |u Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, LE1 9BH, Leicester, UK |4 aut | |
| 700 | 1 | |a Yang |D Shengxiang |u Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, LE1 9BH, Leicester, UK |4 aut | |
| 773 | 0 | |t Soft Computing |d Springer Berlin Heidelberg |g 19/6(2015-06-01), 1511-1522 |x 1432-7643 |q 19:6<1511 |1 2015 |2 19 |o 500 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00500-014-1334-5 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00500-014-1334-5 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Mavrovouniotis |D Michalis |u Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, LE1 9BH, Leicester, UK |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Yang |D Shengxiang |u Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, LE1 9BH, Leicester, UK |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Soft Computing |d Springer Berlin Heidelberg |g 19/6(2015-06-01), 1511-1522 |x 1432-7643 |q 19:6<1511 |1 2015 |2 19 |o 500 | ||