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» Detecting worm variants using machine learning
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129
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JMLR
2010
161views more  JMLR 2010»
14 years 9 months ago
Empirical Bernstein Boosting
Concentration inequalities that incorporate variance information (such as Bernstein's or Bennett's inequality) are often significantly tighter than counterparts (such as...
Pannagadatta K. Shivaswamy, Tony Jebara
148
Voted
SDM
2012
SIAM
216views Data Mining» more  SDM 2012»
13 years 5 months ago
Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
:  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; ...
George Forman
153
Voted
AIHC
2007
Springer
15 years 8 months ago
Affect Detection and an Automated Improvisational AI Actor in E-Drama
Enabling machines to understand emotions and feelings of the human users in their natural language textual input during interaction is a challenging issue in Human Computing. Our w...
Li Zhang, Marco Gillies, John A. Barnden, Robert J...
142
Voted
KDD
2004
ACM
126views Data Mining» more  KDD 2004»
16 years 3 months ago
Selection, combination, and evaluation of effective software sensors for detecting abnormal computer usage
We present and empirically analyze a machine-learning approach for detecting intrusions on individual computers. Our Winnowbased algorithm continually monitors user and system beh...
Jude W. Shavlik, Mark Shavlik
154
Voted
AIRWEB
2008
Springer
15 years 4 months ago
The anti-social tagger: detecting spam in social bookmarking systems
The annotation of web sites in social bookmarking systems has become a popular way to manage and find information on the web. The community structure of such systems attracts spam...
Beate Krause, Christoph Schmitz, Andreas Hotho, Ge...