IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Diffusion Kernels John Lafferty School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 USA lafferty@cs.cmu.edu Guy Lebanon School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 USA lebanon@cs.cmu.edu Abstract A new family of kernels for statistical learning is introduced that ex- ploits the geometric structure of statistical models. Based on the
Riemannian Geometry and Statistical Machine Learning Doctoral Thesis Guy Lebanon Language Technologies Institute School of Computer Science Carnegie Mellon University lebanon@cs.cmu.edu January 31, 2005 Abstract Statistical machine learning algorithms deal with the problem of selecting an appropriate statistical model from a model space Θ based on a training set {xi}N i=1 ⊂ X or {(xi, yi)}N i=1 ⊂
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