Machine Learning & Statistical Inference Lab
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- AMGCF Public
ml-lab-sau/AMGCF’s past year of commit activity - Variants-of-Unsupervised-Feature-Selection Public Forked from Era-Aich/Project_MSC
Implementation of "Entropy Dependency-based Unsupervised Feature Selection" and " Entropy Based Embedded Unsupervised Feature Selection" Papers
ml-lab-sau/Variants-of-Unsupervised-Feature-Selection’s past year of commit activity - SADGSC Public
ml-lab-sau/SADGSC’s past year of commit activity - AGMLGS Public
ml-lab-sau/AGMLGS’s past year of commit activity - UniTSVM Public
ml-lab-sau/UniTSVM’s past year of commit activity - Time-efficient-variants-of-Twin-Extreme-Learning-Machine Public
In this paper, we propose two novel time-efficient formulations of the Twin Extreme Learning Machine, which only require the solution of systems of linear equations for obtaining the final classifier. In this sense, they can combine the benefits of the Twin Support Vector Machine and standard Extreme Learning Machine in the true sense.
ml-lab-sau/Time-efficient-variants-of-Twin-Extreme-Learning-Machine’s past year of commit activity - Multi-label-learning-with-missing-labels-using-sparse-global-structure-for-label-specific-features Public
To deal with the issues emerging from incomplete labels and high-dimensional input space, we propose a multi-label learning approach based on identifying the label-specific features and constraining them with a sparse global structure. The sparse structural constraint helps maintain the typical characteristics of the multi-label learning data.
ml-lab-sau/Multi-label-learning-with-missing-labels-using-sparse-global-structure-for-label-specific-features’s past year of commit activity
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