Computer Science Department
School of Computer Science, Carnegie Mellon University
Learning by Combining Native Features
Mugizi Robert Rwebangira, Avrim Blum
The notion of exploiting data dependent hypothesis spaces is an exciting new direction in machine learning with strong theoretical foundations . A very practical motivation for these techniques is that they allow us to exploit unlabeled data in new ways . 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.