Hash kernels

Journal of Machine Learning Research

Volumn 5, Issue , 2009, Pages 496-503

  Shi, Qinfeng ^a   Petterson, James ^a   Dror, Gideon ^b   Langford, John ^c   Smola, Alex ^c   Strehl, Alex ^c   Vishwanathan, Vishy ^d  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b ACADEMIC COLLEGE OF TEL AVIV YAFFO   (Israel)

^c YAHOO RESEARCH   (United States)

^d PURDUE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STREAM; FEATURE SPACE; KERNEL MATRICES;

SOFTWARE ENGINEERING;

ARTIFICIAL INTELLIGENCE;

EID: 84862300887     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (20)

1. C. Berg, J. P. R. Christensen, and P. Ressel. Harmonic Analysis on Semigroups. Springer, New York, 1984.
2. C. J. C. Burges and B. Schölkopf. Improving the accuracy and speed of support vector learning machines. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, pages 375-381, Cambridge, MA, 1997. MIT Press.
3. S. Chatterjee. Concentration Inequalities with Exchangeable Pairs. PhD thesis, Stanford University, 2005.
4. G. Cormode and M. Muthukrishnan. An improved data stream summary: The count-min sketch and its applications. In LATIN: Latin American Symposium on Theoretical Informatics, 2004.
5. O. Dekel, S. Shalev-Shwartz, and Y. Singer. The Forgetron: A kernel-based perceptron on a fixed budget. In Y. Weiss, B. Schölkopf, and J. Platt, editors, Advances in Neural Information Processing Systems 18, Cambridge, MA, 2006. MIT Press.
6. S. Fine and K. Scheinberg. Efficient SVM training using low-rank kernel representations. JMLR, 2:243- 264, Dec 2001.
7. K. Ganchev and M. Dredze. Small statistical models by random feature mixing. In workshop on Mobile NLP at ACL, 2008.
8. P. Indyk and R. Motawani. Approximate nearest neighbors: Towards removing the curse of dimensionality. In Proceedings of the 30th Symposium on Theory of Computing, pages 604-613, 1998.
9. L. Kontorovich. A universal kernel for learning regular languages. In Machine Learning in Graphs, 2007.
10. J. Langford, L. Li, and A. Strehl. Vowpal wabbit online learning project (technical report). Technical report, 2007.
11. D. Lewis, Y. Yang, T. Rose, and F. Li. Rcv1: A new benchmark collection for text categorization research. The Journal of Machine Learning Research, 5:361-397, 2004.
12. P. Li, K. Church, and T. Hastie. Conditional random sampling: A sketch-based sampling technique for sparse data. In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 873-880. MIT Press, Cambridge, MA, 2007.
13. O. L. Mangasarian. Generalized support vector machines. Technical report, University of Wisconsin, Computer Sciences Department, Madison, 1998.
14. N. Przulj. Biological network comparison using graphlet degree distribution. Bioinformatics, 23(2): e177-e183, Jan 2007.
15. A. Rahimi and B. Recht. Random features for largescale kernel machines. In J. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20. MIT Press, Cambridge, MA, 2008.
16. B. Schölkopf. Support Vector Learning. R. Oldenbourg Verlag, Munich, 1997. Download: http://www.kernel-machines.org.
17. C. H. Teo and S. V. N. Vishwanathan. Fast and space efficient string kernels using suffix arrays. In ICML'06: Proceedings of the 23rd international conference on Machine learning, pages 929-936, New York, NY, USA, 2006. ACM Press. ISBN 1-59593-383-2. doi: http://doi.acm.org/10.1145/1143844.1143961.
18. K. Tsuda, T. Kin, and K. Asai. Marginalized kernels for biological sequences. Bioinformatics, 18 (Suppl. 2):S268-S275, 2002.
19. S. V. N. Vishwanathan, A. J. Smola, and R. Vidal. Binet-Cauchy kernels on dynamical systems and its application to the analysis of dynamic scenes. International Journal of Computer Vision, 73(1):95-119, 2007.
20. C. Watkins. Dynamic alignment kernels. In A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 39-50, Cambridge, MA, 2000. MIT Press.