Hash kernels

Qinfeng Shi; James Petterson; Gideon Dror; John Langford; Alex Smola; Alex Strehl; Vishy Vishwanathan

Hash kernels

Qinfeng Shi^*, James Petterson, Gideon Dror, John Langford, Alex Smola, Alex Strehl, Vishy Vishwanathan

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

42 Citations (Scopus)

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.

Original language	English
Pages (from-to)	496-503
Number of pages	8
Journal	Journal of Machine Learning Research
Volume	5
Publication status	Published - 2009
Event	12th International Conference on Artificial Intelligence and Statistics, AISTATS 2009 - Clearwater, FL, United States Duration: 16 Apr 2009 → 18 Apr 2009

Cite this

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title = "Hash kernels",

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.",

author = "Qinfeng Shi and James Petterson and Gideon Dror and John Langford and Alex Smola and Alex Strehl and Vishy Vishwanathan",

year = "2009",

language = "English",

volume = "5",

pages = "496--503",

journal = "Journal of Machine Learning Research",

issn = "1532-4435",

publisher = "Microtome Publishing",

note = "12th International Conference on Artificial Intelligence and Statistics, AISTATS 2009 ; Conference date: 16-04-2009 Through 18-04-2009",

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TY - JOUR

T1 - Hash kernels

AU - Shi, Qinfeng

AU - Petterson, James

AU - Dror, Gideon

AU - Langford, John

AU - Smola, Alex

AU - Strehl, Alex

AU - Vishwanathan, Vishy

PY - 2009

Y1 - 2009

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=84862300887&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:84862300887

SN - 1532-4435

VL - 5

SP - 496

EP - 503

JO - Journal of Machine Learning Research

JF - Journal of Machine Learning Research

T2 - 12th International Conference on Artificial Intelligence and Statistics, AISTATS 2009

Y2 - 16 April 2009 through 18 April 2009

ER -

Hash kernels

Abstract

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