An Improvement of Extreme Learning Machine for Compact Single-Hidden-Layer Feedforward Neural Networks
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[1] M. Viberg,et al. Adaptive neural nets filter using a recursive Levenberg-Marquardt search direction , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).
[2] Bernard Widrow,et al. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[3] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..
[4] Chee Kheong Siew,et al. Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.
[5] E. Lander,et al. Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.
[6] Guang-Bin Huang,et al. Classification ability of single hidden layer feedforward neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..
[7] S. Sathiya Keerthi,et al. Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms , 2002, IEEE Trans. Neural Networks.
[8] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[9] Thorsten Joachims,et al. Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.
[10] Peter L. Bartlett,et al. The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network , 1998, IEEE Trans. Inf. Theory.
[11] Chih-Jen Lin,et al. A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.
[12] Tommy W. S. Chow,et al. Feedforward networks training speed enhancement by optimal initialization of the synaptic coefficients , 2001, IEEE Trans. Neural Networks.
[13] A. Kai Qin,et al. Evolutionary extreme learning machine , 2005, Pattern Recognit..
[14] D. Serre. Matrices: Theory and Applications , 2002 .
[15] Yudong D. He,et al. Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.
[16] Guang-Bin Huang,et al. Convex Incremental Extreme Learning Machine , 2007 .
[17] Shu-Xia Lu,et al. A comparison among four SVM classification methods: LSVM, NLSVM, SSVM and NSVM , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).
[18] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[19] Sayan Mukherjee,et al. Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.