Evolutionary extreme learning machine

Pattern Recognition

Volumn 38, Issue 10, 2005, Pages 1759-1763

  Zhu, Qin Yu ^a   Qin, A K ^a   Suganthan, P N ^a   Huang, Guang Bin ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Differential evolution; Extreme learning machine; Minimum norm least square

Indexed keywords

BACKPROPAGATION; EVOLUTIONARY ALGORITHMS; FUNCTIONS; ITERATIVE METHODS; NETWORKS (CIRCUITS); NEURAL NETWORKS; OPTIMIZATION; PATTERN RECOGNITION;

DIFFERENTIAL EVOLUTION; EXTREME LEARNING MACHINE (ELM); INPUT WEIGHTS; MINIMUM NORM LEAST SQUARE;

LEARNING ALGORITHMS;

EID: 22844440904     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2005.03.028     Document Type: Article

References (5)

1. G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN2004), Budapest, Hungary, 25-29 July 2004.
2. M.-B. Li, G.-B. Huang, P. Saratchandran, N. Sundararajan, Fully complex extreme learning machine, Neurocomputing, 2005, to appear.
3. R. Ghosh, and B. Verma A hierarchical method for finding optimal architecture and weights using evolutionary least square based learning Int. J. Neural Syst. 12 1 2003 13 24
4. R. Storn, and K. Price Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces J. Global Optim. 11 1997 341 359
5. P.L. Bartlett The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network IEEE Trans. Inform. Theory 44 2 1998 525 536