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ICDM
2006
IEEE
226views Data Mining» more  ICDM 2006»
14 years 1 months ago
Converting Output Scores from Outlier Detection Algorithms into Probability Estimates
Current outlier detection schemes typically output a numeric score representing the degree to which a given observation is an outlier. We argue that converting the scores into wel...
Jing Gao, Pang-Ning Tan
KDD
2009
ACM
180views Data Mining» more  KDD 2009»
14 years 8 months 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
ICRA
2005
IEEE
122views Robotics» more  ICRA 2005»
14 years 1 months ago
Supervised Learning of Places from Range Data using AdaBoost
— This paper addresses the problem of classifying places in the environment of a mobile robot into semantic categories. We believe that semantic information about the type of pla...
Óscar Martínez Mozos, Cyrill Stachni...
PIMRC
2010
IEEE
13 years 5 months ago
Statistical Learning-based Automated Healing: Application to mobility in 3G LTE networks
Abstract--Troubleshooting of wireless networks is a challenging network management task. We have developed, in a previous work, a new troubleshooting methodology, which we named St...
Moazzam Islam Tiwana, Berna Sayraç, Zwi Alt...
CONEXT
2007
ACM
13 years 9 months ago
Detecting worm variants using machine learning
Network intrusion detection systems typically detect worms by examining packet or flow logs for known signatures. Not only does this approach mean worms cannot be detected until ...
Oliver Sharma, Mark Girolami, Joseph S. Sventek