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» Learning and Generalization with the Information Bottleneck
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SIGIR
2008
ACM
14 years 9 months ago
Learning to rank with partially-labeled data
Ranking algorithms, whose goal is to appropriately order a set of objects/documents, are an important component of information retrieval systems. Previous work on ranking algorith...
Kevin Duh, Katrin Kirchhoff
CIKM
2010
Springer
14 years 7 months ago
Combining link and content for collective active learning
In this paper, we study a novel problem Collective Active Learning, in which we aim to select a batch set of "informative" instances from a networking data set to query ...
Lixin Shi, Yuhang Zhao, Jie Tang
EDM
2010
248views Data Mining» more  EDM 2010»
14 years 11 months ago
Analyzing Learning Styles using Behavioral Indicators in Web based Learning Environments
It is argued that the analysis of the learner's generated log files during interactions with a learning environment is necessary to produce interpretative views of their activ...
Nabila Bousbia, Jean-Marc Labat, Amar Balla, Issam...
EEE
2005
IEEE
15 years 3 months ago
From Education to e-Learning : A Union Catalog Service of Learning Resources
Computer-Supported Collaborative Learning (CSCL) is a new learning method that is receiving an increasing amount of attention in Taiwan. With the rapid growth in learning resource...
Pei-Xian Kuo, Jyun-Jie Yan, Jan-Ming Ho
GECCO
2008
Springer
137views Optimization» more  GECCO 2008»
14 years 11 months ago
Informative sampling for large unbalanced data sets
Selective sampling is a form of active learning which can reduce the cost of training by only drawing informative data points into the training set. This selected training set is ...
Zhenyu Lu, Anand I. Rughani, Bruce I. Tranmer, Jos...