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Getting Lost in the Wealth of Classifier Ensembles?
Ludmila Kuncheva, School of Computer Science, Bangor University, United Kingdom
Classifier ensembles have proven their worth for solving challenging problems of modern-day pattern recognition. It is easy to get lost in the massive volume of relevant literature, the quickly growing number of new methods and algorithms being proposed, and the wide spread of the ensemble research into numerous application areas. To be able to benefit from this wealth of knowledge, we need, metaphorically speaking, beacons, anchor points, and a well-organised warehouse. At the current stage, we need tools for assessment, benchmarking, comparison and structuring, more than we need new ensemble methods. In an attempt to address these issues, this talk discussed some of the “anchor points” such as ensemble diversity and combination strategies, and gave a bibliometric perspective on the development of the area.
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Biosignal-based Cognitive Systems and Applications
Tanja Schultz, Cognitive Systems Lab (CSL), University of Bremen, Germany
Biosignals are autonomous signals produced by humans measured in physical quantities. In the context of human-computer interaction, human modalities like speech, gestures or motion, i.e. muscle and brain activity at large, can be captured by non-invasive body-worn sensors. The processing and interpretation of the resulting biosignals offer an inside perspective on human physical and mental activities, intentions, and needs and thus complement the traditional way of observing human interaction from the outside.
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