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TSP
2010
13 years 3 months ago
Learning graphical models for hypothesis testing and classification
Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques...
Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher ...
JMLR
2010
101views more  JMLR 2010»
13 years 3 months ago
Exploiting Feature Covariance in High-Dimensional Online Learning
Some online algorithms for linear classification model the uncertainty in their weights over the course of learning. Modeling the full covariance structure of the weights can prov...
Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer...
RECOMB
2010
Springer
13 years 10 months ago
Leveraging Sequence Classification by Taxonomy-Based Multitask Learning
In this work we consider an inference task that biologists are very good at: deciphering biological processes by bringing together knowledge that has been obtained by experiments u...
Christian Widmer, Jose Leiva, Yasemin Altun, Gunna...
CAIP
2003
Springer
222views Image Analysis» more  CAIP 2003»
14 years 2 months ago
Learning Statistical Structure for Object Detection
Abstract. Many classes of images exhibit sparse structuring of statistical dependency. Each variable has strong statistical dependency with a small number of other variables and ne...
Henry Schneiderman
MM
2010
ACM
238views Multimedia» more  MM 2010»
13 years 9 months ago
Supervised manifold learning for image and video classification
This paper presents a supervised manifold learning model for dimensionality reduction in image and video classification tasks. Unlike most manifold learning models that emphasize ...
Yang Liu, Yan Liu, Keith C. C. Chan