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Generalization Bounds for Indefinite Kernel Machines

Generalization Bounds for Indefinite Kernel Machines

This video was recorded at NIPS Workshop on New Challenges in Theoretical Machine Learning: Learning with Data-dependent Concept Spaces, Whistler 2008. This workshop aims at collecting theoretical insights in the design of data-dependent learning strategies. Specifically we are interested in how far learned prediction rules may be characterized in terms of the observations themselves. This amounts to capturing how well data can be used to construct structured hypothesis spaces for risk minimization strategies - termed empirical hypothesis spaces. Classical analysis of learning algorithms requires the user to define a proper hypothesis space before seeing the data. In practice however, one often decides on the proper learning strategy or the form of the prediction rules of interest after inspection of the data. This theoretical gap constitutes exactly the scope of this workshop. More information about the workshop can be found here.


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