Functional Near-Infrared Spectroscopy and Electroencephalography: A Multimodal Imaging Approach
FAC '09 Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Journal of Real-Time Image Processing
Hybrid ensemble approach for classification
Applied Intelligence
Incremental learning with multi-level adaptation
Neurocomputing
Incremental threshold learning for classifier selection
Neurocomputing
Toward the scalability of neural networks through feature selection
Expert Systems with Applications: An International Journal
A novel online boosting algorithm for automatic anatomy detection
Machine Vision and Applications
Learning to filter spam emails: An ensemble learning approach
International Journal of Hybrid Intelligent Systems
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This paper introduces Learn++, an ensemble of classifiers based algorithm originally developed for incremental learning, and now adapted for information/data fusion applications. Recognizing the conceptual similarity between incremental learning and data fusion, Learn++ follows an alternative approach to data fusion, i.e., sequentially generating an ensemble of classifiers that specifically seek the most discriminating information from each data set. It was observed that Learn++ based data fusion consistently outperforms a similarly configured ensemble classifier trained on any of the individual data sources across several applications. Furthermore, even if the classifiers trained on individual data sources are fine tuned for the given problem, Learn++ can still achieve a statistically significant improvement by combining them, if the additional data sets carry complementary information. The algorithm can also identify-albeit indirectly-those data sets that do not carry such additional information. Finally, it was shown that the algorithm can consecutively learn both the supplementary novel information coming from additional data of the same source, and the complementary information coming from new data sources without requiring access to any of the previously seen data