Hash Kernels

Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, Alex Strehl, S. V. N. Vishwanathan
Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:496-503, 2009.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v5-shi09a, title = {Hash Kernels}, author = {Shi, Qinfeng and Petterson, James and Dror, Gideon and Langford, John and Smola, Alex and Strehl, Alex and Vishwanathan, S. V. N.}, booktitle = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics}, pages = {496--503}, year = {2009}, editor = {van Dyk, David and Welling, Max}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/shi09a/shi09a.pdf}, url = {https://proceedings.mlr.press/v5/shi09a.html}, 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.} }
Endnote
%0 Conference Paper %T Hash Kernels %A Qinfeng Shi %A James Petterson %A Gideon Dror %A John Langford %A Alex Smola %A Alex Strehl %A S. V. N. Vishwanathan %B Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-shi09a %I PMLR %P 496--503 %U https://proceedings.mlr.press/v5/shi09a.html %V 5 %X 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.
RIS
TY - CPAPER TI - Hash Kernels AU - Qinfeng Shi AU - James Petterson AU - Gideon Dror AU - John Langford AU - Alex Smola AU - Alex Strehl AU - S. V. N. Vishwanathan BT - Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-shi09a PB - PMLR DP - Proceedings of Machine Learning Research VL - 5 SP - 496 EP - 503 L1 - http://proceedings.mlr.press/v5/shi09a/shi09a.pdf UR - https://proceedings.mlr.press/v5/shi09a.html AB - 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. ER -
APA
Shi, Q., Petterson, J., Dror, G., Langford, J., Smola, A., Strehl, A. & Vishwanathan, S.V.N.. (2009). Hash Kernels. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 5:496-503 Available from https://proceedings.mlr.press/v5/shi09a.html.

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