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» Evaluating algorithms that learn from data streams
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BTW
2011
Springer
218views Database» more  BTW 2011»
14 years 8 months ago
Tracking Hot-k Items over Web 2.0 Streams
Abstract: The rise of the Web 2.0 has made content publishing easier than ever. Yesterday’s passive consumers are now active users who generate and contribute new data to the web...
Parisa Haghani, Sebastian Michel, Karl Aberer
KDD
1995
ACM
135views Data Mining» more  KDD 1995»
15 years 8 months ago
Rough Sets Similarity-Based Learning from Databases
Manydata mining algorithms developed recently are based on inductive learning methods. Very few are based on similarity-based learning. However, similarity-based learning accrues ...
Xiaohua Hu, Nick Cercone
ICPR
2008
IEEE
15 years 10 months ago
A clustering algorithm combine the FCM algorithm with supervised learning normal mixture model
In this paper we propose a new clustering algorithm which combines the FCM clustering algorithm with the supervised learning normal mixture model; we call the algorithm as the FCM...
Wei Wang, Chunheng Wang, Xia Cui, Ai Wang
NAACL
2010
15 years 2 months ago
Learning Dense Models of Query Similarity from User Click Logs
The goal of this work is to integrate query similarity metrics as features into a dense model that can be trained on large amounts of query log data, in order to rank query rewrit...
Fabio De Bona, Stefan Riezler, Keith Hall, Massimi...
INFOCOM
2010
IEEE
15 years 2 months ago
Chameleon: Adaptive Peer-to-Peer Streaming with Network Coding
—Layered streaming can be used to adapt to the available download capacity of an end-user, and such adaptation is very much required in real world HTTP media streaming. The multi...
Anh Tuan Nguyen, Baochun Li, Frank Eliassen