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JMLR
2010
104views more  JMLR 2010»
14 years 9 months ago
Increasing Feature Selection Accuracy for L1 Regularized Linear Models
L1 (also referred to as the 1-norm or Lasso) penalty based formulations have been shown to be effective in problem domains when noisy features are present. However, the L1 penalty...
Abhishek Jaiantilal, Gregory Z. Grudic
VISAPP
2010
15 years 4 days ago
Inverse Problems in Imaging and Computer Vision - From Regularization Theory to Bayesian Inference
phies are also mentioned and a common mathematical abstraction for all these inverses problems will be presented. By focusing on a simple linear forward model, first a synthetic an...
Ali Mohammad-Djafari
125
Voted
VLSM
2005
Springer
15 years 7 months ago
Color Image Deblurring with Impulsive Noise
Abstract. We propose a variational approach for deblurring and impulsive noise removal in multi-channel images. A robust data fidelity measure and edge preserving regularization a...
Leah Bar, Alexander Brook, Nir A. Sochen, Nahum Ki...
ISCAS
2003
IEEE
201views Hardware» more  ISCAS 2003»
15 years 7 months ago
A regularized simultaneous autoregressive model for texture classification
In this paper, we present a new method for texture classification which we call the regularized simultaneous autoregressive method (RSAR). The regularization technique is introduc...
Yao-wei Wang, Yan-fei Wang, Wen Gao, Yong Xue
PAKDD
2005
ACM
184views Data Mining» more  PAKDD 2005»
15 years 7 months ago
Adjusting Mixture Weights of Gaussian Mixture Model via Regularized Probabilistic Latent Semantic Analysis
Mixture models, such as Gaussian Mixture Model, have been widely used in many applications for modeling data. Gaussian mixture model (GMM) assumes that data points are generated fr...
Luo Si, Rong Jin