Adaptive multimodel knowledge transfer matrix machine for EEG classification
The emerging matrix learning methods have achieved promising performances in
electroencephalogram (EEG) classification by exploiting the structural information between …
electroencephalogram (EEG) classification by exploiting the structural information between …
AdaBoost-based transfer learning with privileged information
B Liu, L Liu, Y **ao, C Liu, X Chen, W Li - Information Sciences, 2022 - Elsevier
Transfer learning aims to improve the learning of the target domain with the help of
knowledge from the source domain. Recently, learning using privileged information (LUPI) …
knowledge from the source domain. Recently, learning using privileged information (LUPI) …
Joint domain adaptation based on adversarial dynamic parameter learning
Domain adaptation aims to improve the performance of the classifier in the target domain by
reducing the difference between the two domains. Domain shifts usually exist in both …
reducing the difference between the two domains. Domain shifts usually exist in both …
Subspace Sequentially Iterative Leaning for Semi-Supervised SVM
Classifying partially labeled high-dimensional data remains a difficult problem for semi-
supervised support vector machine (SVM) since the convergence and the stability can …
supervised support vector machine (SVM) since the convergence and the stability can …
Mutual supervised fusion & transfer learning with interpretable linguistic meaning for social data analytics
Social data analytics is often taken as the most commonly used method for community
discovery, product recommendations, knowledge graph, and so on. In this study, social data …
discovery, product recommendations, knowledge graph, and so on. In this study, social data …