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SIAMIS
2011
8 years 6 months ago
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...
Matthias W. Seeger, Hannes Nickisch
EMNLP
2010
8 years 10 months ago
A Fast Decoder for Joint Word Segmentation and POS-Tagging Using a Single Discriminative Model
We show that the standard beam-search algorithm can be used as an efficient decoder for the global linear model of Zhang and Clark (2008) for joint word segmentation and POS-taggi...
Yue Zhang 0004, Stephen Clark
AUTOMATICA
2002
72views more  AUTOMATICA 2002»
8 years 11 months ago
Quantifying the accuracy of Hammerstein model estimation
: This paper investigates the accuracy of the linear model estimate that forms a part of an overall Hammerstein model structure. A key finding here is that the process of estimatin...
Brett Ninness, Stuart Gibson
CCE
2005
8 years 11 months ago
Integrating CDU, FCC and product blending models into refinery planning
The accuracy of using linear models for crude distillation unit (CDU), fluidize-bed catalytic cracker (FCC) and product blending in refinery planning has been debated for decades....
Wenkai Li, Chi-Wai Hui, AnXue Li
IJCV
2007
159views more  IJCV 2007»
8 years 12 months ago
Face Hallucination: Theory and Practice
In this paper, we study face hallucination, or synthesizing a high-resolution face image from an input low-resolution image, with the help of a large collection of other high-reso...
Ce Liu, Heung-Yeung Shum, William T. Freeman
JMLR
2008
209views more  JMLR 2008»
8 years 12 months ago
Bayesian Inference and Optimal Design for the Sparse Linear Model
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
Matthias W. Seeger
CSDA
2007
137views more  CSDA 2007»
8 years 12 months ago
Fitting finite mixtures of generalized linear regressions in R
R package flexmix provides flexible modelling of finite mixtures of regression models using the EM algorithm. Several new features of the software such as fixed and nested var...
Bettina Grün, Friedrich Leisch
CORR
2006
Springer
106views Education» more  CORR 2006»
8 years 12 months ago
Reasoning with Intervals on Granules
: The formalizations of periods of time inside a linear model of Time are usually based on the notion of intervals, that may contain or may not their endpoints. This is not enough ...
Sylviane R. Schwer
BMCBI
2006
100views more  BMCBI 2006»
8 years 12 months ago
Empirical array quality weights in the analysis of microarray data
Background: Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or &qu...
Matthew E. Ritchie, Dileepa S. Diyagama, Jody Neil...
BMCBI
2006
103views more  BMCBI 2006»
8 years 12 months ago
Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression
Background: The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level ...
Sébastien Lemieux
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