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» Information Theory, Inference, and Learning Algorithms
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ICPR
2006
IEEE
14 years 9 months ago
Exploiting the Geometry of Gene Expression Patterns for Unsupervised Learning
Typical gene expression clustering algorithms are restricted to a specific underlying pattern model while overlooking the possibility that other information carrying patterns may ...
Rave Harpaz, Robert M. Haralick
SIGIR
2002
ACM
13 years 7 months ago
A new family of online algorithms for category ranking
We describe a new family of topic-ranking algorithms for multi-labeled documents. The motivation for the algorithms stems from recent advances in online learning algorithms. The a...
Koby Crammer, Yoram Singer
CVPR
2008
IEEE
14 years 10 months ago
Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition
We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this a...
Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang ...
NIPS
2004
13 years 9 months ago
Semi-Markov Conditional Random Fields for Information Extraction
We describe semi-Markov conditional random fields (semi-CRFs), a conditionally trained version of semi-Markov chains. Intuitively, a semiCRF on an input sequence x outputs a "...
Sunita Sarawagi, William W. Cohen
ER
2003
Springer
119views Database» more  ER 2003»
14 years 1 months ago
Toward the Automatic Derivation of XML Transformations
Existing solutions to data and schema integration require user interaction/input to generate a data transformation between two different schemas. These approaches are not appropri...
Martin Erwig