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» Practical Online Active Learning for Classification
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ICML
2002
IEEE
14 years 7 months ago
Active + Semi-supervised Learning = Robust Multi-View Learning
In a multi-view problem, the features of the domain can be partitioned into disjoint subsets (views) that are sufficient to learn the target concept. Semi-supervised, multi-view a...
Ion Muslea, Steven Minton, Craig A. Knoblock
PAKDD
2011
ACM
473views Data Mining» more  PAKDD 2011»
13 years 12 days ago
 Finding Rare Classes: Adapting Generative and Discriminative Models in Active Learning
Discovering rare categories and classifying new instances of them is an important data mining issue in many fields, but fully supervised learning of a rare class classifier is pr...
Timothy Hospedales, Shaogang Gong and Tao Xiang
ISWC
2003
IEEE
13 years 12 months ago
Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing
Context-aware computing describes the situation where a wearable / mobile computer is aware of its user’s state and surroundings and modifies its behavior based on this informat...
Andreas Krause, Daniel P. Siewiorek, Asim Smailagi...
ICALT
2009
IEEE
13 years 10 months ago
Using Students' Devices and a No-to-Low Cost Online Tool to Support Interactive Experiential mLearning
The rapid evolution and ubiquitous use of mobile devices is an historical opportunity to improve experiential interactivity in education practices to support “deep” learning. ...
Andrew Litchfield, Ryszard Raban, Laurel Evelyn Dy...
ICML
2000
IEEE
14 years 7 months ago
Less is More: Active Learning with Support Vector Machines
We describe a simple active learning heuristic which greatly enhances the generalization behavior of support vector machines (SVMs) on several practical document classification ta...
Greg Schohn, David Cohn