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JMLR
2006
124views more  JMLR 2006»
15 years 3 months ago
A Direct Method for Building Sparse Kernel Learning Algorithms
Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature sp...
Mingrui Wu, Bernhard Schölkopf, Gökhan H...
208
Voted
SIAMIS
2011
14 years 10 months ago
Gradient-Based Methods for Sparse Recovery
The convergence rate is analyzed for the sparse reconstruction by separable approximation (SpaRSA) algorithm for minimizing a sum f(x) + ψ(x), where f is smooth and ψ is convex, ...
William W. Hager, Dzung T. Phan, Hongchao Zhang
DATE
2008
IEEE
75views Hardware» more  DATE 2008»
15 years 9 months ago
Temperature Control of High-Performance Multi-core Platforms Using Convex Optimization
With technology advances, the number of cores integrated on a chip and their speed of operation is increasing. This, in turn is leading to a significant increase in chip temperat...
Srinivasan Murali, Almir Mutapcic, David Atienza, ...
CDC
2010
IEEE
176views Control Systems» more  CDC 2010»
14 years 10 months ago
Random projection algorithms for convex set intersection problems
The focus of this paper is on the set intersection problem for closed convex sets admitting projection operation in a closed form. The objective is to investigate algorithms that w...
Angelia Nedic
CDC
2008
IEEE
133views Control Systems» more  CDC 2008»
15 years 3 months ago
A nonlinear least squares estimation procedure without initial parameter guesses
Abstract-- This paper introduces a convex formulation approach for the initialization of parameter estimation problems (PEP). The proposed method exploits the parameter-affine feat...
Julian Bonilla Alarcon, Moritz Diehl, Bart De Moor...