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» An Overview of Associative Classifiers
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ACSAC
2004
IEEE
15 years 1 months ago
High-Fidelity Modeling of Computer Network Worms
modeling, such as using epidemic models, has been the general method of choice for understanding and analyzing the high-level effects of worms. However, high-fidelity models, such...
Kalyan S. Perumalla, Srikanth Sundaragopalan
AUSDM
2006
Springer
124views Data Mining» more  AUSDM 2006»
15 years 1 months ago
Analyzing Harmonic Monitoring Data Using Data Mining
Harmonic monitoring has become an important tool for harmonic management in distribution systems. A comprehensive harmonic monitoring program has been designed and implemented on ...
Ali Asheibi, David Stirling, Danny Soetanto
ECML
2006
Springer
15 years 1 months ago
Margin-Based Active Learning for Structured Output Spaces
In many complex machine learning applications there is a need to learn multiple interdependent output variables, where knowledge of these interdependencies can be exploited to impr...
Dan Roth, Kevin Small
70
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FLAIRS
2008
14 years 12 months ago
Contrast Pattern Mining with Gap Constraints for Peptide Folding Prediction
1 In this paper, we propose a peptide folding prediction method which discovers contrast patterns to differentiate and predict peptide folding classes. A contrast pattern is defin...
Chinar C. Shah, Xingquan Zhu, Taghi M. Khoshgoftaa...
CONEXT
2007
ACM
14 years 11 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