%0 Journal Article %1 959248 %A Gärtner, Thomas %C New York, NY, USA %D 2003 %I ACM %J SIGKDD Explor. Newsl. %K Kernel %N 1 %P 49--58 %R 10.1145/959242.959248 %T A survey of kernels for structured data %U http://portal.acm.org/citation.cfm?doid=959242.959248 %V 5 %X 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.

@article{959248, 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.}, added-at = {2010-07-27T17:21:24.000+0200}, address = {New York, NY, USA}, author = {G\"{a}rtner, Thomas}, biburl = {https://www.bibsonomy.org/bibtex/202ed899eed46b754ee9c3914459db6e5/macek}, description = {A survey of kernels for structured data}, doi = {10.1145/959242.959248}, interhash = {6c0747dee495ca023eb3863aa7164684}, intrahash = {02ed899eed46b754ee9c3914459db6e5}, issn = {1931-0145}, journal = {SIGKDD Explor. Newsl.}, keywords = {Kernel}, number = 1, pages = {49--58}, publisher = {ACM}, timestamp = {2010-09-11T18:39:44.000+0200}, title = {A survey of kernels for structured data}, url = {http://portal.acm.org/citation.cfm?doid=959242.959248}, volume = 5, year = 2003 }