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» Learning and Generalization with the Information Bottleneck
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CHI
2011
ACM
14 years 1 months ago
Apolo: making sense of large network data by combining rich user interaction and machine learning
Extracting useful knowledge from large network datasets has become a fundamental challenge in many domains, from scientific literature to social networks and the web. We introduc...
Duen Horng Chau, Aniket Kittur, Jason I. Hong, Chr...
CIKM
2011
Springer
13 years 10 months ago
Citation count prediction: learning to estimate future citations for literature
In most of the cases, scientists depend on previous literature which is relevant to their research fields for developing new ideas. However, it is not wise, nor possible, to trac...
Rui Yan, Jie Tang, Xiaobing Liu, Dongdong Shan, Xi...
RECSYS
2009
ACM
15 years 4 months ago
Learning to recommend with trust and distrust relationships
With the exponential growth of Web contents, Recommender System has become indispensable for discovering new information that might interest Web users. Despite their success in th...
Hao Ma, Michael R. Lyu, Irwin King
SIGIR
2009
ACM
15 years 4 months ago
The importance of manual assessment in link discovery
Using a ground truth extracted from the Wikipedia, and a ground truth created through manual assessment, we show that the apparent performance advantage seen in machine learning a...
Darren Wei Che Huang, Andrew Trotman, Shlomo Geva
SIGIR
2008
ACM
14 years 10 months ago
Semi-supervised spam filtering: does it work?
The results of the 2006 ECML/PKDD Discovery Challenge suggest that semi-supervised learning methods work well for spam filtering when the source of available labeled examples diff...
Mona Mojdeh, Gordon V. Cormack