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WWW
2004
ACM
16 years 13 days ago
Accurate, scalable in-network identification of p2p traffic using application signatures
The ability to accurately identify the network traffic associated with different P2P applications is important to a broad range of network operations including application-specifi...
Subhabrata Sen, Oliver Spatscheck, Dongmei Wang
KDD
2009
ACM
180views Data Mining» more  KDD 2009»
16 years 9 days ago
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Sofus A. Macskassy
KDD
2008
ACM
137views Data Mining» more  KDD 2008»
16 years 5 days ago
Learning classifiers from only positive and unlabeled data
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
Charles Elkan, Keith Noto
KDD
2008
ACM
104views Data Mining» more  KDD 2008»
16 years 5 days ago
Learning methods for lung tumor markerless gating in image-guided radiotherapy
In an idealized gated radiotherapy treatment, radiation is delivered only when the tumor is at the right position. For gated lung cancer radiotherapy, it is difficult to generate ...
Ying Cui, Jennifer G. Dy, Gregory C. Sharp, Brian ...
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
16 years 5 days 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