Two-Stage Learning Kernel Algorithms
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.
Research Areas
