A survey of kernels for structured data

Authors:
Thomas Gärtner
Affiliations:
University of Bristol, United Kingdom
Venue:
ACM SIGKDD Explorations Newsletter
Year:
2003

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Abstract

Kernel methods in general and support vector machines in particular have been successful in various learning tasks on data represented in a single table. Much 'real-world' data, however, is structured - it has no natural representation in a single table. Usually, to apply kernel methods to 'real-world' data, extensive pre-processing is performed to embed the data into areal vector space and thus in a single table. This survey describes several approaches of defining positive definite kernels on structured instances directly.