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» Selective Attention Improves Learning
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AAAI
2007
15 years 3 months ago
Actively Exploring Creation of Face Space(s) for Improved Face Recognition
We propose a learning framework that actively explores creation of face space(s) by selecting images that are complementary to the images already represented in the face space. We...
Nitesh V. Chawla, Kevin W. Bowyer
108
Voted
ICMLA
2009
14 years 10 months ago
Improving Clinical Relevance in Ensemble Support Vector Machine Models of Radiation Pneumonitis Risk
Patients undergoing thoracic radiation therapy can develop radiation pneumonitis (RP), a potentially fatal inflammation of the lungs. Support vector machines (SVMs), a statistical...
Todd W. Schiller, Yixin Chen, Issam El-Naqa, Josep...
81
Voted
EMNLP
2009
14 years 10 months ago
How well does active learning
Machine involvement has the potential to speed up language documentation. We assess this potential with timed annotation experiments that consider annotator expertise, example sel...
Jason Baldridge, Alexis Palmer
TWC
2010
14 years 7 months ago
Splitting algorithms for fast relay selection: generalizations, analysis, and a unified view
Relay selection for cooperative communications promises significant performance improvements, and is, therefore, attracting, considerable attention. While several criteria have be...
Virag Shah, Neelesh B. Mehta, Raymond Yim
108
Voted
ML
2000
ACM
157views Machine Learning» more  ML 2000»
15 years 14 days ago
A Multistrategy Approach to Classifier Learning from Time Series
We present an approach to inductive concept learning using multiple models for time series. Our objective is to improve the efficiency and accuracy of concept learning by decomposi...
William H. Hsu, Sylvian R. Ray, David C. Wilkins