We address the problem of training the free parameters of a statistical machine translation system. We show significant improvements over a state-of-the-art minimum error rate tr...
We consider a kernel-based approach to nonlinear classification that coordinates the generation of “synthetic” points (to be used in the kernel) with “chunking” (working wi...
Abstract--Recently, sparse approximation has become a preferred method for learning large scale kernel machines. This technique attempts to represent the solution with only a subse...
This paper presents a decomposition method for efficiently constructing 1-norm Support Vector Machines (SVMs). The decomposition algorithm introduced in this paper possesses many d...