Tuning the Feature Space using Support Vector Control
نویسندگان
چکیده
The fraction of training data chosen as support vectors gives us an idea of the complexity of the concept being modeled. Controlling the number of support vectors and their relevance allows us to control the complexity of the concept learnt. In this paper we present a method to approximately control the fraction of support vectors. We demonstrate it’s applicability to the tuning of the feature space where the projected data can be separated more effectively. We also outline other problems where the same approach can be useful.
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