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» A Framework for Multiple-Instance Learning
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SIAMIS
2011
13 years 2 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
WWW
2011
ACM
13 years 2 months ago
Pragmatic evaluation of folksonomies
Recently, a number of algorithms have been proposed to obtain hierarchical structures — so-called folksonomies — from social tagging data. Work on these algorithms is in part ...
Denis Helic, Markus Strohmaier, Christoph Trattner...
PRL
2010
149views more  PRL 2010»
13 years 2 months ago
Adaptive linear models for regression: Improving prediction when population has changed
The general setting of regression analysis is to identify a relationship between a response variable Y and one or several explanatory variables X by using a learning sample. In a ...
Charles Bouveyron, Julien Jacques
SIAMIS
2010
378views more  SIAMIS 2010»
13 years 2 months ago
Global Interactions in Random Field Models: A Potential Function Ensuring Connectedness
Markov random field (MRF) models, including conditional random field models, are popular in computer vision. However, in order to be computationally tractable, they are limited to ...
Sebastian Nowozin, Christoph H. Lampert
TIP
2010
255views more  TIP 2010»
13 years 2 months ago
Image Super-Resolution Via Sparse Representation
This paper presents a new approach to single-image superresolution, based on sparse signal representation. Research on image statistics suggests that image patches can be wellrepre...
Jianchao Yang, John Wright, Thomas S. Huang, Yi Ma