Sciweavers

61 search results - page 5 / 13
» Active Learning to Maximize Area Under the ROC Curve
Sort
View
SDM
2009
SIAM
154views Data Mining» more  SDM 2009»
14 years 4 months ago
AMORI: A Metric-Based One Rule Inducer.
The requirements of real-world data mining problems vary extensively. It is plausible to assume that some of these requirements can be expressed as application-specific performan...
Niklas Lavesson, Paul Davidsson
CLEF
2011
Springer
12 years 7 months ago
Detecting Wikipedia Vandalism using Machine Learning - Notebook for PAN at CLEF 2011
Wikipedia vandalism identification is a very complex issue, which is now mostly solved manually by volunteers. This paper presents the main components of a system built by our grou...
Cristian-Alexandru Dragusanu, Marina Cufliuc, Adri...
IJON
2010
148views more  IJON 2010»
13 years 6 months ago
Modeling radiation-induced lung injury risk with an ensemble of support vector machines
Radiation-induced lung injury, radiation pneumonitis (RP), is a potentially fatal side-effect of thoracic radiation therapy. In this work, using an ensemble of support vector mac...
Todd W. Schiller, Yixin Chen, Issam El-Naqa, Josep...
IJPRAI
2010
151views more  IJPRAI 2010»
13 years 6 months ago
Structure-Embedded AUC-SVM
: AUC-SVM directly maximizes the area under the ROC curve (AUC) through minimizing its hinge loss relaxation, and the decision function is determined by those support vector sample...
Yunyun Wang, Songcan Chen, Hui Xue
JMLR
2006
132views more  JMLR 2006»
13 years 7 months ago
Learning to Detect and Classify Malicious Executables in the Wild
We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious execu...
Jeremy Z. Kolter, Marcus A. Maloof