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.
Keywords
Computer scienceTable (database)Kernel (algebra)Kernel methodRepresentation (politics)Support vector machineReal world dataArtificial intelligenceData miningMachine learningData scienceMathematics
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Publication Info
- Year
- 2003
- Type
- article
- Volume
- 5
- Issue
- 1
- Pages
- 49-58
- Citations
- 445
- Access
- Closed
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Cite This
Thomas Gärtner
(2003).
A survey of kernels for structured data.
ACM SIGKDD Explorations Newsletter
, 5
(1)
, 49-58.
https://doi.org/10.1145/959242.959248
Identifiers
- DOI
- 10.1145/959242.959248