"Research is creating new Knowledge." Neil Armstrong
The software found here comes with no warranty. It is available for non-commercial research purposes only under the GNU General Public License. However, not withstanding any provision of the GNU General Public License, the software may not be used for commercial purposes without explicit written permission.
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S. Mehrkanoon, J. A. K. Suykens, "Multi-Label Semi-Supervised Learning using Regularized Kernel Spectral Clustering'', in Proc. of International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, Jul.2016, pp. 4009-4016. [PDF][Matlab Code]
S. Mehrkanoon, M. Agudelo, J. A. K. Suykens, "Incremental multi-class semi-supervised clustering regularized by Kalman filtering'', Neural Networks, Vol. 71, Aug.2015, pp. 88-104. [PDF][Datasets]
S. Mehrkanoon, X. Huang, J.A.K. Suykens, "Non-parallel support vector classifiers with different loss functions", Neurocomputing, Vol. 143, Nov.2014, pp. 294-301.[PDF][Matlab Code]
S. Mehrkanoon, T. Falck, J. A. K. Suykens, “Parameter estimation for time varying dynamical systems using least squares support vector machines,” In proc. of the 16th IFAC Symposium on System Identification (SYSID), Jul. 2012, Brussels, Belgium, pp. 1300-1305. [PDF][Matlab Code]
S. Mehrkanoon, J.A.K. Suykens, "Learning Solutions to Partial Differential Equations using LS-SVM'', Neurocomputing, Vol. 159, 105-116, 2015. [PDF][Matlab Code]
S. Mehrkanoon, J. A. K. Suykens, "LS-SVM approximate solution to linear time varying descriptor systems", Automatica, 48(10), 2502-2511, 2012. [PDF][Matlab Code]
S. Mehrkanoon, T. Falck, J. A. K. Suykens, "Approximate solutions to ordinary differential equations using least squares support vector machines", IEEE Trans. Neural Netw. Learning Syst, 23(9), 1356-1367, 2012. [PDF][Matlab Code]
S. Mehrkanoon, "A direct variable step block multistep method for solving general third-order ODEs", Numerical Algorithms, 57(1), 53-66, 2011. [PDF][C Code]