The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A survey of kernels for structured data
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.
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Cites
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Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 4,941
Neural Network for Graphs: A Contextual Constructive Approach
Micheli, A. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2010350 · Cited by 639
Metadata
- Authors
- Gärtner, T.
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2003-07
- Volume
- 5
- Issue
- 1
- Pages
- 49-58
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2131479089
- Cited by
- 452
- References
- 3
- FWCI
- 22.26
- Type
- journal article
- Language
- en
- Open access
- closed
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