Two-Stage Learning Kernel Algorithms
Venue
Proceedings of the 27th Annual International Conference on Machine Learning (ICML 2010)
Publication Year
2010
Authors
Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
BibTeX
Abstract
This paper examines two-stage techniques for learning kernels based on a notion of
alignment. It presents a number of novel theoretical, algorithmic, and empirical
results for alignmentbased techniques. Our results build on previous work by
Cristianini et al. (2001), but we adopt a different definition of kernel alignment
and significantly extend that work in several directions: we give a novel and
simple concentration bound for alignment between kernel matrices; show the
existence of good predictors for kernels with high alignment, both for
classification and for regression; give algorithms for learning a maximum alignment
kernel by showing that the problem can be reduced to a simple QP; and report the
results of extensive experimentswith this alignment-based method in classification
and regression tasks, which show an improvement both over the uniformcombination of
kernels and over other state-of-the-art learning kernel methods.
