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» Entropy Numbers, Operators and Support Vector Kernels
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ISCA
2000
IEEE
78views Hardware» more  ISCA 2000»
13 years 11 months ago
Vector instruction set support for conditional operations
Vector instruction sets are receiving renewed interest because of their applicability to multimedia. Current multimedia instruction sets use short vectors with SIMD implementation...
James E. Smith, Greg Faanes, Rabin A. Sugumar
SP
2006
IEEE
193views Security Privacy» more  SP 2006»
14 years 1 months ago
Analysis of the Linux Random Number Generator
Linux is the most popular open source project. The Linux random number generator is part of the kernel of all Linux distributions and is based on generating randomness from entrop...
Zvi Gutterman, Benny Pinkas, Tzachy Reinman
JMLR
2006
150views more  JMLR 2006»
13 years 7 months ago
Building Support Vector Machines with Reduced Classifier Complexity
Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overc...
S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCos...
DAGM
2004
Springer
13 years 11 months ago
MinOver Revisited for Incremental Support-Vector-Classification
The well-known and very simple MinOver algorithm is reformulated for incremental support vector classification with and without kernels. A modified proof for its O(t-1/2 ) converge...
Thomas Martinetz
ICANN
2007
Springer
14 years 1 months ago
Sparse Least Squares Support Vector Regressors Trained in the Reduced Empirical Feature Space
Abstract. In this paper we discuss sparse least squares support vector regressors (sparse LS SVRs) defined in the reduced empirical feature space, which is a subspace of mapped tr...
Shigeo Abe, Kenta Onishi