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» Evaluating algorithms that learn from data streams
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WWW
2008
ACM
15 years 10 months ago
Learning to classify short and sparse text & web with hidden topics from large-scale data collections
This paper presents a general framework for building classifiers that deal with short and sparse text & Web segments by making the most of hidden topics discovered from larges...
Xuan Hieu Phan, Minh Le Nguyen, Susumu Horiguchi
79
Voted
WWW
2009
ACM
15 years 10 months ago
Learning consensus opinion: mining data from a labeling game
We consider the problem of identifying the consensus ranking for the results of a query, given preferences among those results from a set of individual users. Once consensus ranki...
Paul N. Bennett, David Maxwell Chickering, Anton M...
71
Voted
ISMB
2003
14 years 11 months ago
Evaluation of text data mining for database curation: lessons learned from the KDD Challenge Cup
Alexander S. Yeh, Lynette Hirschman, Alexander A. ...
SIGMOD
2007
ACM
164views Database» more  SIGMOD 2007»
15 years 10 months ago
Fast data stream algorithms using associative memories
The primary goal of data stream research is to develop space and time efficient solutions for answering continuous online summarization queries. Research efforts over the last dec...
Nagender Bandi, Ahmed Metwally, Divyakant Agrawal,...
MM
2010
ACM
137views Multimedia» more  MM 2010»
14 years 10 months ago
Self-diagnostic peer-assisted video streaming through a learning framework
Quality control and resource optimization are challenging problems in peer-assisted video streaming systems, due to their large scales and unreliable peer behavior. Such systems a...
Di Niu, Baochun Li, Shuqiao Zhao