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TIP
2008
128views more  TIP 2008»
15 years 7 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
16 years 1 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»
15 years 7 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»
16 years 16 days 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
16 years 1 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...