Hash Kernels for Structured Data

Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, S.V.N. Vishwanathan

JMLR 2009 pp. 2615-2637

/jmlr/2009/shi2009jmlr-hash/

Abstract

We propose hashing to facilitate efficient kernels. This generalizes previous work using sampling and we show a principled way to compute the kernel matrix for data streams and sparse feature spaces. Moreover, we give deviation bounds from the exact kernel matrix. This has applications to estimation on strings and graphs.

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Cite

Text

Shi et al. "Hash Kernels for Structured Data." Journal of Machine Learning Research, 2009.

Markdown

[Shi et al. "Hash Kernels for Structured Data." Journal of Machine Learning Research, 2009.](https://mlanthology.org/jmlr/2009/shi2009jmlr-hash/)

BibTeX

@article{shi2009jmlr-hash,
  title     = {{Hash Kernels for Structured Data}},
  author    = {Shi, Qinfeng and Petterson, James and Dror, Gideon and Langford, John and Smola, Alex and Vishwanathan, S.V.N.},
  journal   = {Journal of Machine Learning Research},
  year      = {2009},
  pages     = {2615-2637},
  volume    = {10},
  url       = {https://mlanthology.org/jmlr/2009/shi2009jmlr-hash/}
}