The notion of exploiting data dependent hypothesis spaces is an exciting new direction in machine learning with strong theoretical foundations [66]. A very practical motivation for these techniques is that they allow us to exploit unlabeled data in new ways [2]. In this work we investigate a particular technique for combining "native" features with features derived from a similarity function. We also describe a novel technique for using unlabeled data to define a similarity function.

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