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114
Voted
KDD
2009
ACM
227views Data Mining» more  KDD 2009»
16 years 1 months ago
Efficiently learning the accuracy of labeling sources for selective sampling
Many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. However, what if there are multiple labeling sources (`oracles...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
97
Voted
KDD
2009
ACM
174views Data Mining» more  KDD 2009»
16 years 1 months ago
Audience selection for on-line brand advertising: privacy-friendly social network targeting
This paper describes and evaluates privacy-friendly methods for extracting quasi-social networks from browser behavior on user-generated content sites, for the purpose of finding ...
Foster J. Provost, Brian Dalessandro, Rod Hook, Xi...
115
Voted
KDD
2009
ACM
243views Data Mining» more  KDD 2009»
16 years 1 months ago
Exploiting Wikipedia as external knowledge for document clustering
In traditional text clustering methods, documents are represented as "bags of words" without considering the semantic information of each document. For instance, if two ...
Xiaohua Hu, Xiaodan Zhang, Caimei Lu, E. K. Park, ...
158
Voted
KDD
2008
ACM
284views Data Mining» more  KDD 2008»
16 years 1 months ago
Community evolution in dynamic multi-mode networks
A multi-mode network typically consists of multiple heterogeneous social actors among which various types of interactions could occur. Identifying communities in a multi-mode netw...
Lei Tang, Huan Liu, Jianping Zhang, Zohreh Nazeri
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
16 years 1 months ago
Practical learning from one-sided feedback
In many data mining applications, online labeling feedback is only available for examples which were predicted to belong to the positive class. Such applications include spam filt...
D. Sculley