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» Learning from Highly Structured Data by Decomposition
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151
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ICCV
2007
IEEE
16 years 5 months ago
Steerable Random Fields
In contrast to traditional Markov random field (MRF) models, we develop a Steerable Random Field (SRF) in which the field potentials are defined in terms of filter responses that ...
Stefan Roth, Michael J. Black
178
Voted
ICDCS
2012
IEEE
13 years 6 months ago
Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data
Operational network data, management data such as customer care call logs and equipment system logs, is a very important source of information for network operators to detect prob...
Chi-Yao Hong, Matthew Caesar, Nick G. Duffield, Ji...
147
Voted
BMCBI
2006
180views more  BMCBI 2006»
15 years 3 months ago
The Gaggle: An open-source software system for integrating bioinformatics software and data sources
Background: Systems biologists work with many kinds of data, from many different sources, using a variety of software tools. Each of these tools typically excels at one type of an...
Paul T. Shannon, David J. Reiss, Richard Bonneau, ...
142
Voted
BMCBI
2010
160views more  BMCBI 2010»
15 years 3 months ago
Annotation of gene promoters by integrative data-mining of ChIP-seq Pol-II enrichment data
Background: Use of alternative gene promoters that drive widespread cell-type, tissue-type or developmental gene regulation in mammalian genomes is a common phenomenon. Chromatin ...
Ravi Gupta, Priyankara Wikramasinghe, Anirban Bhat...
171
Voted
AMFG
2003
IEEE
244views Biometrics» more  AMFG 2003»
15 years 9 months ago
Manifold of Facial Expression
In this paper, we propose the concept of Manifold of Facial Expression based on the observation that images of a subject’s facial expressions define a smooth manifold in the hig...
Ya Chang, Changbo Hu, Matthew Turk