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» Analysis of Functional MRI Data Using Mutual Information
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BMCBI
2006
119views more  BMCBI 2006»
15 years 3 months ago
Hidden Markov Model Variants and their Application
Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way...
Stephen Winters-Hilt
BMCBI
2005
112views more  BMCBI 2005»
15 years 3 months ago
Visualization methods for statistical analysis of microarray clusters
Background: The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determin...
Matthew A. Hibbs, Nathaniel C. Dirksen, Kai Li, Ol...
153
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COLING
2008
15 years 4 months ago
Homotopy-Based Semi-Supervised Hidden Markov Models for Sequence Labeling
This paper explores the use of the homotopy method for training a semi-supervised Hidden Markov Model (HMM) used for sequence labeling. We provide a novel polynomial-time algorith...
Gholamreza Haffari, Anoop Sarkar
155
Voted
TMC
2010
137views more  TMC 2010»
15 years 1 months ago
A Metric for DISH Networks: Analysis, Implications, and Applications
—In wireless networks, node cooperation has been exploited as a data relaying mechanism for decades. However, the wireless channel allows for much richer interaction among nodes....
Tie Luo, Vikram Srinivasan, Mehul Motani
127
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GIS
2002
ACM
15 years 3 months ago
Design of field wrappers for mobile field data collection
In mobile field data collection, data gatherer requires to get geospatial information service to support in navigating to the right locations, describing context of observed objec...
Peisheng Zhao, Sarah Nusser, Leslie L. Miller