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» Learning to Identify Unexpected Instances in the Test Set
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UAI
2004
13 years 8 months ago
Active Model Selection
Classical learning assumes the learner is given a labeled data sample, from which it learns a model. The field of Active Learning deals with the situation where the learner begins...
Omid Madani, Daniel J. Lizotte, Russell Greiner
SIGMOD
2010
ACM
174views Database» more  SIGMOD 2010»
14 years 6 days ago
Sampling dirty data for matching attributes
We investigate the problem of creating and analyzing samples of relational databases to find relationships between string-valued attributes. Our focus is on identifying attribute...
Henning Köhler, Xiaofang Zhou, Shazia Wasim S...
ACL
2004
13 years 8 months ago
Relieving the data Acquisition Bottleneck in Word Sense Disambiguation
Supervised learning methods for WSD yield better performance than unsupervised methods. Yet the availability of clean training data for the former is still a severe challenge. In ...
Mona T. Diab
CVPR
2010
IEEE
13 years 11 months ago
On the design of robust classifiers for computer vision
The design of robust classifiers, which can contend with the noisy and outlier ridden datasets typical of computer vision, is studied. It is argued that such robustness requires l...
Hamed Masnadi-Shirazi, Nuno Vasconcelos, Vijay Mah...
ECTEL
2006
Springer
13 years 11 months ago
Getting to Know Your Student in Distance Learning Contexts
Abstract. Good teachers know their students, and exploit this knowledge to adapt or optimise their instruction. Teachers know their students because they interact with them face-to...
Claus Zinn, Oliver Scheuer