TY - GEN
T1 - Two-stage learning kernel algorithms
AU - Cortes, Corinna
AU - Mohri, Mehryar
AU - Rostamizadeh, Afshin
PY - 2010
Y1 - 2010
N2 - 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 alignment- based 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 experiments with this alignment-based method in classification and regression tasks, which show an improvement both over the uniform combination of kernels and over other state-of-the-art learning kernel methods.
AB - 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 alignment- based 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 experiments with this alignment-based method in classification and regression tasks, which show an improvement both over the uniform combination of kernels and over other state-of-the-art learning kernel methods.
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M3 - Conference contribution
AN - SCOPUS:77956505061
SN - 9781605589077
T3 - ICML 2010 - Proceedings, 27th International Conference on Machine Learning
SP - 239
EP - 246
BT - ICML 2010 - Proceedings, 27th International Conference on Machine Learning
T2 - 27th International Conference on Machine Learning, ICML 2010
Y2 - 21 June 2010 through 25 June 2010
ER -