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KDD
2008
ACM
140views Data Mining» more  KDD 2008»
14 years 10 months ago
Semi-supervised approach to rapid and reliable labeling of large data sets
Supervised classification methods have been shown to be very effective for a large number of applications. They require a training data set whose instances are labeled to indicate...
György J. Simon, Vipin Kumar, Zhi-Li Zhang
KDD
2009
ACM
133views Data Mining» more  KDD 2009»
14 years 10 months ago
On the tradeoff between privacy and utility in data publishing
In data publishing, anonymization techniques such as generalization and bucketization have been designed to provide privacy protection. In the meanwhile, they reduce the utility o...
Tiancheng Li, Ninghui Li
IJNSEC
2008
210views more  IJNSEC 2008»
13 years 9 months ago
A Method for Locating Digital Evidences with Outlier Detection Using Support Vector Machine
One of the biggest challenges facing digital investigators is the sheer volume of data that must be searched in locating the digital evidence. How to efficiently locate the eviden...
Zaiqiang Liu, Dongdai Lin, Fengdeng Guo
SIGKDD
2000
87views more  SIGKDD 2000»
13 years 9 months ago
Integrating Data Mining into Vertical Solutions
At KDD-99, the panel on Integrating Data Mining into Vertical Solutions addressed a series of questions regarding future trends in industrial applications. Panelists were chosen t...
Ron Kohavi, Mehran Sahami
KDD
2008
ACM
207views Data Mining» more  KDD 2008»
14 years 10 months ago
Active learning with direct query construction
Active learning may hold the key for solving the data scarcity problem in supervised learning, i.e., the lack of labeled data. Indeed, labeling data is a costly process, yet an ac...
Charles X. Ling, Jun Du