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» An Instance Selection Approach to Multiple Instance Learning
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KDD
2009
ACM
229views Data Mining» more  KDD 2009»
15 years 10 months ago
Relational learning via latent social dimensions
Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather than classical IID distribution. To address the interdependency among data instances, ...
Lei Tang, Huan Liu
SIGMOD
2008
ACM
131views Database» more  SIGMOD 2008»
15 years 10 months ago
Discovering topical structures of databases
The increasing complexity of enterprise databases and the prevalent lack of documentation incur significant cost in both understanding and integrating the databases. Existing solu...
Wensheng Wu, Berthold Reinwald, Yannis Sismanis, R...
CCR
2006
76views more  CCR 2006»
14 years 9 months ago
Secure distributed data-mining and its application to large-scale network measurements
The rapid growth of the Internet over the last decade has been startling. However, efforts to track its growth have often fallen afoul of bad data -- for instance, how much traffi...
Matthew Roughan, Yin Zhang
KDD
2009
ACM
198views Data Mining» more  KDD 2009»
15 years 10 months ago
Heterogeneous source consensus learning via decision propagation and negotiation
Nowadays, enormous amounts of data are continuously generated not only in massive scale, but also from different, sometimes conflicting, views. Therefore, it is important to conso...
Jing Gao, Wei Fan, Yizhou Sun, Jiawei Han
DIS
2008
Springer
14 years 11 months ago
Empirical Asymmetric Selective Transfer in Multi-objective Decision Trees
We consider learning tasks where multiple target variables need to be predicted. Two approaches have been used in this setting: (a) build a separate single-target model for each ta...
Beau Piccart, Jan Struyf, Hendrik Blockeel