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TSP
2008
114views more  TSP 2008»
13 years 4 months ago
Universal Switching Linear Least Squares Prediction
We consider sequential regression of individual sequences under the square error loss. Using a competitive algorithm framework, we construct a sequential algorithm that can achieve...
Suleyman Serdar Kozat, Andrew C. Singer
NN
2010
Springer
189views Neural Networks» more  NN 2010»
12 years 11 months ago
Sparse kernel learning with LASSO and Bayesian inference algorithm
Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been investigated in two separate recent papers (Gao et al., 2008) and (Wang et al., 2007). This paper is co...
Junbin Gao, Paul W. Kwan, Daming Shi
CEC
2010
IEEE
12 years 11 months ago
Parameter estimation with term-wise decomposition in biochemical network GMA models by hybrid regularized Least Squares-Particle
High-throughput analytical techniques such as nuclear magnetic resonance, protein kinase phosphorylation, and mass spectroscopic methods generate time dense profiles of metabolites...
Prospero C. Naval, Luis G. Sison, Eduardo R. Mendo...
CVPR
2004
IEEE
14 years 6 months ago
3D Human Pose from Silhouettes by Relevance Vector Regression
We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor pr...
Ankur Agarwal, Bill Triggs
BMCBI
2006
201views more  BMCBI 2006»
13 years 4 months ago
Gene selection algorithms for microarray data based on least squares support vector machine
Background: In discriminant analysis of microarray data, usually a small number of samples are expressed by a large number of genes. It is not only difficult but also unnecessary ...
E. Ke Tang, Ponnuthurai N. Suganthan, Xin Yao