Hash Kernels for Structured Data

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. [abs] [ pdf ][ bib ] &copy JMLR 2009. ( edit, beta )

Authors

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

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