Multi-Instance Kernels

T Gartner, PA Flach, A Kowalczyk, AJ Smola

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

Learning from structured data is becoming increasingly important. However, most prior work on kernel methods has focused on learning from attribute-value data. Only recently, research started investigating kernels for structured data. This paper considers kernels for multi-instance problems - a class of concepts on individuals represented by sets. The main result of this paper is a kernel on multi-instance data that can be shown to separate positive and negative sets under natural assumptions. This kernel compares favorably with state of the art multi-instance learning algorithms in an empirical study. Finally, we give some concluding remarks and propose future work that might further improve the results.
Translated title of the contributionMulti-Instance Kernels
Original languageEnglish
Title of host publicationUnknown
EditorsClaude Sammut, Achim Hoffmann
PublisherMorgan Kaufmann
Pages179 - 186
Number of pages7
ISBN (Print)1558608737
Publication statusPublished - Jul 2002

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the 19th International Conference on Machine Learning

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