Titlebook: Human Interaction with Machines; Proceedings of the 6 G. Hommel,Sheng Huanye Conference proceedings 2006 Springer Science+Business Media B.

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978-90-481-7021-0Springer Science+Business Media B.V. 2006

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A note on the Berlin Brain-Computer Interface,This paper discusses machine learning methods and their application to Brain-Computer Interfacing. A particular focus is placed on linear classification methods which can be applied in the BCI context. Finally, we provide an overview on the Berlin-Brain Computer Interface (BBCI).

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Reference Points for the Observation of Systems Behaviour,ral concept of reference points in the context of complex systems and their development could be given. The paper discusses three approaches for the observation of systems behaviour and draws some conclusions in view of such concept.

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G. Hommel,Sheng HuanyeState of the art research results in the areas of:.Observation of systems behavior.Multi-modal User-Interfaces.Self-healing Software.Brain-Computer Interfaces.Human-machine interaction.Multi-modal mac

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Conference proceedings 2006o Tong University and Technische Universit?t Berlin. The goal of those workshops is to bring together researchers from both universities in order to present research results to an international community. The series of workshops started in 1990 with the International Workshop on “Artificial Intellig

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Robust Analysis and Interpretation of Spoken Chinese Queries,anisms, i.e., robust semantic parsing and robust interpretation, and an additional component as well, i.e., recognition error correction. A preliminary evaluation demonstrates that a good robustness is achieved efficiently under this framework.

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omputer Interfaces.Human-machine interaction.Multi-modal macThe International Workshop on “Human Interaction with Machines” is the sixth in a successful series of workshops that were established by Shanghai Jiao Tong University and Technische Universit?t Berlin. The goal of those workshops is to bri

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Efficient Text Categorization Using a Min-Max Modular Support Vector Machine,ne task decomposition to improve generalization performance. The experimental results indicate that the new method has better generalization performance than traditional SVMs and previous M.-SVMs using random task decomposition, and is much faster than traditional SVMs.