Two-stage learning kernel algorithms

Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 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.

Original languageEnglish (US)
Title of host publicationICML 2010 - Proceedings, 27th International Conference on Machine Learning
Pages239-246
Number of pages8
StatePublished - Sep 17 2010
Event27th International Conference on Machine Learning, ICML 2010 - Haifa, Israel
Duration: Jun 21 2010Jun 25 2010

Publication series

NameICML 2010 - Proceedings, 27th International Conference on Machine Learning

Other

Other27th International Conference on Machine Learning, ICML 2010
CountryIsrael
CityHaifa
Period6/21/106/25/10

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Education

Cite this

Cortes, C., Mohri, M., & Rostamizadeh, A. (2010). Two-stage learning kernel algorithms. In ICML 2010 - Proceedings, 27th International Conference on Machine Learning (pp. 239-246). (ICML 2010 - Proceedings, 27th International Conference on Machine Learning).