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» Results of the KDD'99 Classifier Learning
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MCS
2009
Springer
14 years 1 months ago
Incremental Learning of Variable Rate Concept Drift
We have recently introduced an incremental learning algorithm, Learn++ .NSE, for Non-Stationary Environments, where the data distribution changes over time due to concept drift. Le...
Ryan Elwell, Robi Polikar
ISMIS
2005
Springer
14 years 2 months ago
Scalable Inductive Learning on Partitioned Data
With the rapid advancement of information technology, scalability has become a necessity for learning algorithms to deal with large, real-world data repositories. In this paper, sc...
Qijun Chen, Xindong Wu, Xingquan Zhu
CVPR
2009
IEEE
15 years 4 months ago
Visual Tracking with Online Multiple Instance Learning
In this paper, we address the problem of learning an adaptive appearance model for object tracking. In particular, a class of tracking techniques called “tracking by detectionâ...
Boris Babenko, Ming-Hsuan Yang, Serge J. Belongie
CLEF
2007
Springer
14 years 3 months ago
MIRACLE at ImageCLEFanot 2007: Machine Learning Experiments on Medical Image Annotation
This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2007. Our areas of expertise do not include image...
Sara Lana-Serrano, Julio Villena-Román, Jos...
AIEDU
2010
13 years 6 months ago
Supporting Collaborative Learning and E-Discussions Using Artificial Intelligence Techniques
An emerging trend in classrooms is the use of networked visual argumentation tools that allow students to discuss, debate, and argue with one another in a synchronous fashion about...
Bruce M. McLaren, Oliver Scheuer, Jan Miksatko