[ Overview | Participants | Funding ]
Learning becomes easy once the correct input representation has been chosen, for example, one that produces linearly seperable point sets. We have several projects in the direction of (1) automatically generating intermediate representations to ais supervised learning algorithms (2) developing methods that allow the use of relational representations and of learning relational definitions, and (3) developing a flexible knowledge representation language that can be used along with feature efficient learning algorithms. We study applications of this general knowledge representation paradigm in the context of learning in the natural language domain (e.g., information extraction) and visual recognition. (4) Developing kernels for Boolean functions and relational functions and studying computational complexity of algorithms that use kernels.