Subject
Applied Mathematics,Computational Theory and Mathematics,Computer Networks and Communications,Theoretical Computer Science
Reference16 articles.
1. D. Achlioptas, Database-friendly random projections, 20th Annual Symposium on Principles of Database Systems, Santa Barbara, CA, 2001, pp. 274–281.
2. R.I. Arriaga, S. Vempala, An algorithmic theory of learning: robust concepts and random projection, 40th Annual Symposium on Foundations of Computer Science, New York, NY, 1999, IEEE Computer Society Press, Los Alamitos, CA, 1999, pp. 616–623.
3. S. Arora, R. Kannan, Learning mixtures of arbitrary Gaussians, 33rd Annual ACM Symposium on Theory of Computing, Creete, Greece, ACM, New York, 2001, pp. 247–257.
4. S. Dasgupta, Learning mixtures of Gaussians, 40th Annual Symposium on Foundations of Computer Science, New York, NY, 1999, IEEE Computer Society Press, Los Alamitos, CA, 1999, pp. 634–644.
5. S. Dasgupta, A. Gupta, An elementary proof of the Johnson–Lindenstrauss lemma. Technical Report 99-006, UC Berkeley, March 1999.
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