Learning a kernel function for classification with small training samples

Tomer Hertz, Aharon Bar Hillel, Daphna Weinshall

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

27 Scopus citations

Abstract

When given a small sample, we show that classification with SVM can be considerably enhanced by using a kernel function learned from the training data prior to discrimination. This kernel is also shown to enhance retrieval based on data similarity. Specifically, we describe KerndBoost - a boosting algorithm which computes a kernel function as a combination of 'weak' space partitions. The kernel learning method naturally incorporates domain knowledge in the form of unlabeled data (i.e. in a semi-supervised or transductive settings), and also in the form of labeled samples from relevant related problems (i.e. in a learning-to-learn scenario). The latter goal is accomplished by learning a single kernel function for all classes. We show comparative evaluations of our method on datasets from the UCI repository. We demonstrate performance enhancement on two challenging tasks: digit classification with kernel SVM, and facial image retrieval based on image similarity as measured by the learnt kernel.

Original languageEnglish
Title of host publicationACM International Conference Proceeding Series - Proceedings of the 23rd International Conference on Machine Learning, ICML 2006
Pages401-408
Number of pages8
DOIs
StatePublished - 1 Dec 2006
Externally publishedYes
Event23rd International Conference on Machine Learning, ICML 2006 - Pittsburgh, PA, United States
Duration: 25 Jun 200629 Jun 2006

Publication series

NameACM International Conference Proceeding Series
Volume148

Conference

Conference23rd International Conference on Machine Learning, ICML 2006
Country/TerritoryUnited States
CityPittsburgh, PA
Period25/06/0629/06/06

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