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» Learning and Generalization with the Information Bottleneck
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KDD
2007
ACM
178views Data Mining» more  KDD 2007»
16 years 7 months ago
Practical learning from one-sided feedback
In many data mining applications, online labeling feedback is only available for examples which were predicted to belong to the positive class. Such applications include spam filt...
D. Sculley
ICRA
2010
IEEE
104views Robotics» more  ICRA 2010»
15 years 5 months ago
Using model knowledge for learning inverse dynamics
— In recent years, learning models from data has become an increasingly interesting tool for robotics, as it allows straightforward and accurate model approximation. However, in ...
Duy Nguyen-Tuong, Jan Peters
172
Voted
PKDD
2009
Springer
120views Data Mining» more  PKDD 2009»
16 years 1 months ago
Variational Graph Embedding for Globally and Locally Consistent Feature Extraction
Existing feature extraction methods explore either global statistical or local geometric information underlying the data. In this paper, we propose a general framework to learn fea...
Shuang-Hong Yang, Hongyuan Zha, Shaohua Kevin Zhou...
CIKM
2011
Springer
14 years 6 months ago
Toward interactive training and evaluation
Machine learning often relies on costly labeled data, and this impedes its application to new classification and information extraction problems. This has motivated the developme...
Gregory Druck, Andrew McCallum
189
Voted
ACMIDC
2010
15 years 10 months ago
Paper-based multimedia interaction as learning tool for disabled children
The purpose of our research is to support cognitive, motor, and emotional development of severely disabled children in the school context. We designed and implemented a set of nov...
Franca Garzotto, Manuel Bordogna