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» A Framework for Identifying Textual Redundancy
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BMCBI
2007
166views more  BMCBI 2007»
13 years 7 months ago
How to decide which are the most pertinent overly-represented features during gene set enrichment analysis
Background: The search for enriched features has become widely used to characterize a set of genes or proteins. A key aspect of this technique is its ability to identify correlati...
Roland Barriot, David J. Sherman, Isabelle Dutour
ACII
2011
Springer
12 years 7 months ago
Predicting Facial Indicators of Confusion with Hidden Markov Models
Affect plays a vital role in learning. During tutoring, particular affective states may benefit or detract from student learning. A key cognitiveaffective state is confusion, which...
Joseph F. Grafsgaard, Kristy Elizabeth Boyer, Jame...
KDD
2003
ACM
214views Data Mining» more  KDD 2003»
14 years 8 months ago
Adaptive duplicate detection using learnable string similarity measures
The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied...
Mikhail Bilenko, Raymond J. Mooney
CVPR
2010
IEEE
14 years 3 months ago
Learning from Interpolated Images using Neural Networks for Digital Forensics
Interpolated images have data redundancy, and special correlation exists among neighboring pixels, which is a crucial clue in digital forensics. We design a neural network based f...
Yizhen Huang, Na Fan
ISNN
2005
Springer
14 years 1 months ago
Feature Selection and Intrusion Detection Using Hybrid Flexible Neural Tree
Current Intrusion Detection Systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything)...
Yuehui Chen, Ajith Abraham, Ju Yang