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[Cru10] Kernel Approximation by Locality Sensitive Hashing with Application to Active Learning
Rapport Scientifique : Date de dépot: 2010/01/01, Nb pages 10, (Tech. Rep.: CEDRIC-10-1898)Mots clés: LSH, active learning, kernel approximation, scalable active learning, scalability
Résumé:
Locality Sensitive Hashing (LSH) methods are being successfully employed for scaling similarity queries or similarity joins to large databases. We show that operations requiring intensive kernel computations can also benefit from the use of LSH. Specifically, this allows to accelerate active learning methods for which the query corresponds to a class boundary.
Equipe:
vertigo
BibTeX
| @techreport { | |||
| Cru10, | |||
| title | = | "{Kernel Approximation by Locality Sensitive Hashing with Application to Active Learning}", | |
| author | = | "M. Crucianu", | |
| number | = | "{CEDRIC-10-1898}", | |
| institution | = | "{CEDRIC laboratory, CNAM-Paris, France}", | |
| date | = | {01-01-2010}, | |
| pages | = | "10", | |
| } | |||
