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1
Hidden space support vector machines.
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2
Reproducing chaos by variable structure recurrent neural networks.
IEEE Trans Neural Netw. 2004 Nov;15(6):1450-7. doi: 10.1109/TNN.2004.836236.
3
Rigorous proof of termination of SMO algorithm for support vector machines.
IEEE Trans Neural Netw. 2005 May;16(3):774-6. doi: 10.1109/TNN.2005.844857.
4
Universal approximation of extreme learning machine with adaptive growth of hidden nodes.
IEEE Trans Neural Netw Learn Syst. 2012 Feb;23(2):365-71. doi: 10.1109/TNNLS.2011.2178124.
5
The pre-image problem in kernel methods.
IEEE Trans Neural Netw. 2004 Nov;15(6):1517-25. doi: 10.1109/TNN.2004.837781.
6
Recurrent kernel machines: computing with infinite echo state networks.
Neural Comput. 2012 Jan;24(1):104-33. doi: 10.1162/NECO_a_00200. Epub 2011 Aug 18.
7
Associative memory design using support vector machines.
IEEE Trans Neural Netw. 2006 Sep;17(5):1165-74. doi: 10.1109/TNN.2006.877539.
8
Generalized core vector machines.
IEEE Trans Neural Netw. 2006 Sep;17(5):1126-40. doi: 10.1109/TNN.2006.878123.
9
Kernel map compression for speeding the execution of kernel-based methods.
IEEE Trans Neural Netw. 2011 Jun;22(6):870-9. doi: 10.1109/TNN.2011.2127485. Epub 2011 May 5.
10
Comparing support vector machines and feedforward neural networks with similar hidden-layer weights.
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