Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification

Article English OPEN
Ye, Qing; Pan, Hao; Liu, Changhua;
(2015)
  • Publisher: Hindawi Publishing Corporation
  • Journal: Computational Intelligence and Neuroscience,volume 2,015 (issn: 1687-5265, eissn: 1687-5273)
  • Publisher copyright policies & self-archiving
  • Related identifiers: pmc: PMC4477444, doi: 10.1155/2015/731494
  • Subject: R858-859.7 | Research Article | Computer applications to medicine. Medical informatics | Neurosciences. Biological psychiatry. Neuropsychiatry | RC321-571 | Article Subject
    acm: ComputingMethodologies_PATTERNRECOGNITION

A novel semisupervised extreme learning machine (ELM) with clustering discrimination manifold regularization (CDMR) framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clusterin... View more
  • References (24)
    24 references, page 1 of 3

    Huang, G.-B.. An insight into extreme learning machines: random neurons, random features and kernels. Cognitive Computation . 2014; 6 (3): 376-390

    Huang, G.-B., Zhou, H., Ding, X., Zhang, R.. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics . 2012; 42 (2): 513-529

    Berk, R. A.. Support vector machines. Statistical Learning from a Regression Perspective . 2008: 1-28

    Lim, G.-M., Bae, D.-M., Kim, J.-H.. Fault diagnosis of rotating machine by thermography method on support vector machine. Journal of Mechanical Science and Technology . 2014; 28 (8): 2947-2952

    Belkin, M., Sindhwani, V., Niyogi, P.. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research . 2006; 7: 2399-2434

    Zhu, X., Goldberg, A. B.. Introduction to Semi-Supervised Learning . 2009

    Melacci, S., Belkin, M.. Laplacian support vector machines trained in the primal. Journal of Machine Learning Research . 2011; 12: 1149-1184

    Chen, W.-J., Shao, Y.-H., Hong, N.. Laplacian smooth twin support vector machine for semi-supervised classification. International Journal of Machine Learning and Cybernetics . 2014; 5 (3): 459-468

    Zhou, Y., Liu, B., Xia, S.. Semi-supervised extreme learning machine with manifold and pairwise constraints regularization. Neurocomputing . 2015; 149: 180-186

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