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TSP
2010
14 years 8 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 ...
PR
2011
14 years 4 months ago
A survey of multilinear subspace learning for tensor data
Increasingly large amount of multidimensional data are being generated on a daily basis in many applications. This leads to a strong demand for learning algorithms to extract usef...
Haiping Lu, Konstantinos N. Plataniotis, Anastasio...
CVPR
2007
IEEE
16 years 4 months ago
Learning the Compositional Nature of Visual Objects
The compositional nature of visual objects significantly limits their representation complexity and renders learning of structured object models tractable. Adopting this modeling ...
Björn Ommer, Joachim M. Buhmann
CVPR
2007
IEEE
16 years 4 months ago
Adaptive Patch Features for Object Class Recognition with Learned Hierarchical Models
We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width...
Fabien Scalzo, Justus H. Piater
KDD
2003
ACM
157views Data Mining» more  KDD 2003»
16 years 2 months ago
Cross-training: learning probabilistic mappings between topics
Classification is a well-established operation in text mining. Given a set of labels A and a set DA of training documents tagged with these labels, a classifier learns to assign l...
Sunita Sarawagi, Soumen Chakrabarti, Shantanu Godb...