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TIP
2008
128views more  TIP 2008»
14 years 11 months ago
Wavelet Frame Accelerated Reduced Support Vector Machines
In this paper, a novel method for reducing the runtime complexity of a support vector machine classifier is presented. The new training algorithm is fast and simple. This is achiev...
Matthias Rätsch, Gerd Teschke, Sami Romdhani,...
RTSS
2007
IEEE
15 years 6 months ago
Distributed Minimal Time Convergecast Scheduling for Small or Sparse Data Sources
— Many applications of sensor networks require the base station to collect all the data generated by sensor nodes. As a consequence many-to-one communication pattern, referred to...
Ying Zhang, Shashidhar Gandham, Qingfeng Huang
SIAMJO
2008
93views more  SIAMJO 2008»
14 years 11 months ago
Smooth Optimization with Approximate Gradient
We show that the optimal complexity of Nesterov's smooth first-order optimization algorithm is preserved when the gradient is only computed up to a small, uniformly bounded er...
Alexandre d'Aspremont
NAA
2004
Springer
178views Mathematics» more  NAA 2004»
15 years 5 months ago
Performance Optimization and Evaluation for Linear Codes
In this paper, we develop a probabilistic model for estimation of the numbers of cache misses during the sparse matrix-vector multiplication (for both general and symmetric matrice...
Pavel Tvrdík, Ivan Simecek
ICASSP
2009
IEEE
15 years 6 months ago
Map approach to learning sparse Gaussian Markov networks
Recently proposed l1-regularized maximum-likelihood optimization methods for learning sparse Markov networks result into convex problems that can be solved optimally and efficien...
Narges Bani Asadi, Irina Rish, Katya Scheinberg, D...