We consider a framework for semi-supervised learning using spectral decomposition-based unsupervised kernel design. We relate this approach to previously proposed semi-supervised l...
Online learning algorithms have impressive convergence properties when it comes to risk minimization and convex games on very large problems. However, they are inherently sequenti...
Daniel Hsu, Nikos Karampatziakis, John Langford, A...
Many traditional methods for shape classification involve
establishing point correspondences between shapes to
produce matching scores, which are in turn used as similarity
meas...
Reinforcement learning algorithms can become unstable when combined with linear function approximation. Algorithms that minimize the mean-square Bellman error are guaranteed to co...
This paper considers a method for learning a distance metric in a fingerprinting system which identifies a query content by measuring the distance between the fingerprint of th...