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» Exploiting multiple classifier types with active learning
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ICML
2010
IEEE
13 years 7 months ago
Active Learning for Networked Data
We introduce a novel active learning algorithm for classification of network data. In this setting, training instances are connected by a set of links to form a network, the label...
Mustafa Bilgic, Lilyana Mihalkova, Lise Getoor
AAAI
2010
13 years 8 months ago
Community-Guided Learning: Exploiting Mobile Sensor Users to Model Human Behavior
Modeling human behavior requires vast quantities of accurately labeled training data, but for ubiquitous people-aware applications such data is rarely attainable. Even researchers...
Daniel Peebles, Hong Lu, Nicholas D. Lane, Tanzeem...
CVPR
1998
IEEE
14 years 8 months ago
Using Adaptive Tracking to Classify and Monitor Activities in a Site
We describe a vision system that monitors activity in a site over extended periods of time. The system uses a distributed set of sensors to cover the site, and an adaptive tracker...
W. Eric L. Grimson, Chris Stauffer, R. Romano, L. ...
ICML
2002
IEEE
14 years 7 months ago
Cranking: Combining Rankings Using Conditional Probability Models on Permutations
A new approach to ensemble learning is introduced that takes ranking rather than classification as fundamental, leading to models on the symmetric group and its cosets. The approa...
Guy Lebanon, John D. Lafferty
AMDO
2010
Springer
13 years 4 months ago
Multiple-Activity Human Body Tracking in Unconstrained Environments
We propose a method for human full-body pose tracking from measurements of wearable inertial sensors. Since the data provided by such sensors is sparse, noisy and often ambiguous, ...
Loren Arthur Schwarz, Diana Mateus, Nassir Navab