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How meta-heuristic algorithms contribute to deep learning in the hype of big data analytics Conference paper
Advances in Intelligent Systems and Computing
Authors:  Fong S.;  Deb S.;  Yang X.-S.
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Algorithm design  Deep learning  Meta-heuristic algorithm  Nature-inspired computing algorithms  Neural network training  
Learning the distribution of data for embedding Conference paper
Proceedings - 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016
Authors:  Shen Y.;  Ren P.;  Zhang T.;  Tang Y.Y.
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Dimensionality reduction  Distribution preserving embedding  Kernel density estimation  
Lightweight feature selection methods based on standardized measure of dispersion for mining big data Conference paper
2016 IEEE International Conference on Computer and Information Technology (CIT), Nadi, Fiji, 8-10 Dec. 2016
Authors:  Simon Fong;  Robert P. Biuk-Aghai;  Yain-Whar Si
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Big Data  Classification  Feature Selection  
Nonlinear random vibration of the cable modeled as MDOF system and excited by filtered Gaussian white noise Conference paper
Proceedings of the ECCOMAS Thematic Conference on Multibody Dynamics 2015, Multibody Dynamics 2015
Authors:  Er G.K.;  Iu V.P.;  Wang K.
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Cable  Fokker-Planck-Kolmogorov equation  High dimensionality  Nonlinear  Random vibration  State-space-split method  
Incorporating Local and Global Geometric Structure for Hyperspectral Image Classification Conference paper
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, San Diego, CA, 5-8 Oct. 2014
Authors:  Huiwu Luo;  Yuan Yan Tang;  Lina Yang
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Swarm Search for Feature Selection in Classification Conference paper
Proceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013, Sydney, NSW, Australia, 3-5 Dec. 2013
Authors:  Simon Fong;  Xin-She Yang;  Suash Deb
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Classification  Feature Selection  Metaheuristic  
Feature extraction based on discriminant analysis with penalty constra int for hyperspectral image cl assification Conference paper
Proceedings - International Conference on Machine Learning and Cybernetics
Authors:  Luo H.W.;  Yang L.N.;  Li Y.-M.;  Yuan H.L.;  Tang Y.Y.
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Dimension reduction  Feature extraction  Hyperspectral image classification  Linear discriminant analysis  Penalty discriminant analysis  
Nonparametric Feature Extraction via Direct Maximum Margin Alignment Conference paper
Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011, Honolulu, HI, 18-21 Dec. 2011
Authors:  Miao Cheng;  Yuan Yan Tang;  Chi-Man Pun
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Dimensionality Reduction  Direct Maximum Margin Alignment  Kernel-view  Nonparametric Feature Extraction  Subspace Selection  
A linear subspace learning algorithm for incremental data Conference paper
2009 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2009
Authors:  Fang B.;  Chen J.;  Tang Y.-Y.
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Dimensionality reduction  Incremental learning  Incremental maximum margin criterion (IMMC)  Linear discriminant analysis (LDA)  Maximum margin criterion (MMC)  
Discriminant Isomap projection Conference paper
2009 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2009
Authors:  Zheng Y.-L.;  Zhang T.-P.;  Fang B.;  Tang Y.-Y.
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Classification  Dimensionality reduction  Projection  Supervised