Kernel Design using Boosting

Koby Crammer, J. Keshet, Yoram Singer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The focus of the paper is the problem of learning kernel operators from empirical data. We cast the kernel design problem as the construction of an accurate kernel from simple (and less accurate) base kernels. We use the boosting paradigm to perform the kernel construction process. To do so, we modify the booster so as to accommodate kernel operators. We also devise an efficient weak-learner for simple kernels that is based on generalized eigen vector decomposition. We demonstrate the effectiveness of our approach on synthetic data and on the USPS dataset. On the USPS dataset, the performance of the Perceptron algorithm with learned kernels is systematically better than a fixed RBF kernel.
Original languageAmerican English
Title of host publicationThe 16th Annual Conference on Neural Information Processing Systems (NIPS)
StatePublished - 2002

Bibliographical note

Place of conference:N/A


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