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SIAMSC
2008
198views more  SIAMSC 2008»
13 years 7 months ago
Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space
A model-constrained adaptive sampling methodology is proposed for reduction of large-scale systems with high-dimensional parametric input spaces. Our model reduction method uses a ...
T. Bui-Thanh, Karen Willcox, Omar Ghattas
JMLR
2006
150views more  JMLR 2006»
13 years 7 months ago
Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization
Support vector machines utilizing the 1-norm, typically set up as linear programs (Mangasarian, 2000; Bradley and Mangasarian, 1998), are formulated here as a completely unconstra...
Olvi L. Mangasarian
SIAMMAX
2010
92views more  SIAMMAX 2010»
13 years 2 months ago
Estimating the Backward Error in LSQR
We propose practical stopping criteria for the iterative solution of sparse linear least squares (LS) problems. Although we focus our discussion on the algorithm LSQR of Paige and ...
Pavel Jiránek, David Titley-Péloquin
KDD
2007
ACM
159views Data Mining» more  KDD 2007»
14 years 8 months ago
Local decomposition for rare class analysis
Given its importance, the problem of predicting rare classes in large-scale multi-labeled data sets has attracted great attentions in the literature. However, the rare-class probl...
Junjie Wu, Hui Xiong, Peng Wu, Jian Chen
BMCBI
2004
113views more  BMCBI 2004»
13 years 7 months ago
Oligo kernels for datamining on biological sequences: a case study on prokaryotic translation initiation sites
Background: Kernel-based learning algorithms are among the most advanced machine learning methods and have been successfully applied to a variety of sequence classification tasks ...
Peter Meinicke, Maike Tech, Burkhard Morgenstern, ...