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» Ensemble Learning of Regional Classifiers
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CSIE
2009
IEEE
14 years 3 months ago
Building a General Purpose Cross-Domain Sentiment Mining Model
Building a model using machine learning that can classify the sentiment of natural language text often requires an extensive set of labeled training data from the same domain as t...
Matthew Whitehead, Larry Yaeger
SDM
2010
SIAM
218views Data Mining» more  SDM 2010»
13 years 10 months ago
Confidence-Based Feature Acquisition to Minimize Training and Test Costs
We present Confidence-based Feature Acquisition (CFA), a novel supervised learning method for acquiring missing feature values when there is missing data at both training and test...
Marie desJardins, James MacGlashan, Kiri L. Wagsta...
CVPR
2008
IEEE
14 years 10 months ago
Multiple-instance ranking: Learning to rank images for image retrieval
We study the problem of learning to rank images for image retrieval. For a noisy set of images indexed or tagged by the same keyword, we learn a ranking model from some training e...
Yang Hu, Mingjing Li, Nenghai Yu
KDD
2003
ACM
129views Data Mining» more  KDD 2003»
14 years 8 months ago
Empirical comparisons of various voting methods in bagging
Finding effective methods for developing an ensemble of models has been an active research area of large-scale data mining in recent years. Models learned from data are often subj...
Kelvin T. Leung, Douglas Stott Parker Jr.
BMCBI
2006
139views more  BMCBI 2006»
13 years 8 months ago
DNA Molecule Classification Using Feature Primitives
Background: We present a novel strategy for classification of DNA molecules using measurements from an alpha-Hemolysin channel detector. The proposed approach provides excellent c...
Raja Tanveer Iqbal, Matthew Landry, Stephen Winter...