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» Generalizing over Several Learning Settings
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KDD
2005
ACM
147views Data Mining» more  KDD 2005»
14 years 2 months ago
Combining proactive and reactive predictions for data streams
Mining data streams is important in both science and commerce. Two major challenges are (1) the data may grow without limit so that it is difficult to retain a long history; and (...
Ying Yang, Xindong Wu, Xingquan Zhu
MM
2004
ACM
152views Multimedia» more  MM 2004»
14 years 2 months ago
Manifold-ranking based image retrieval
In this paper, we propose a novel transductive learning framework named manifold-ranking based image retrieval (MRBIR). Given a query image, MRBIR first makes use of a manifold ra...
Jingrui He, Mingjing Li, HongJiang Zhang, Hanghang...
SDM
2012
SIAM
216views Data Mining» more  SDM 2012»
11 years 11 months ago
Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
:  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; ...
George Forman
SIGSOFT
2009
ACM
14 years 9 months ago
Automatic steering of behavioral model inference
Many testing and analysis techniques use finite state models to validate and verify the quality of software systems. Since the specification of such models is complex and timecons...
David Lo, Leonardo Mariani, Mauro Pezzè
KDD
2009
ACM
164views Data Mining» more  KDD 2009»
14 years 9 months ago
Social influence analysis in large-scale networks
In large social networks, nodes (users, entities) are influenced by others for various reasons. For example, the colleagues have strong influence on one's work, while the fri...
Jie Tang, Jimeng Sun, Chi Wang, Zi Yang