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» Boosting Methods for Regression
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JMLR
2008
151views more  JMLR 2008»
13 years 10 months ago
Learning to Combine Motor Primitives Via Greedy Additive Regression
The computational complexities arising in motor control can be ameliorated through the use of a library of motor synergies. We present a new model, referred to as the Greedy Addit...
Manu Chhabra, Robert A. Jacobs
PROMISE
2010
13 years 4 months ago
How effective is Tabu search to configure support vector regression for effort estimation?
Background. Recent studies have shown that Support Vector Regression (SVR) has an interesting potential in the field of effort estimation. However applying SVR requires to careful...
Anna Corazza, Sergio Di Martino, Filomena Ferrucci...
TSP
2010
13 years 4 months ago
Distributed sparse linear regression
The Lasso is a popular technique for joint estimation and continuous variable selection, especially well-suited for sparse and possibly under-determined linear regression problems....
Gonzalo Mateos, Juan Andrés Bazerque, Georg...
CVPR
2008
IEEE
15 years 3 days ago
Conditional density learning via regression with application to deformable shape segmentation
Many vision problems can be cast as optimizing the conditional probability density function p(C|I) where I is an image and C is a vector of model parameters describing the image. ...
Jingdan Zhang, Shaohua Kevin Zhou, Dorin Comaniciu...
IJCNN
2006
IEEE
14 years 4 months ago
Greedy forward selection algorithms to Sparse Gaussian Process Regression
Abstract— This paper considers the basis vector selection issue invloved in forward selection algorithms to sparse Gaussian Process Regression (GPR). Firstly, we re-examine a pre...
Ping Sun, Xin Yao