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» Learn to Detect Phishing Scams Using Learning and Ensemble
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ICML
2009
IEEE
14 years 6 months ago
Identifying suspicious URLs: an application of large-scale online learning
This paper explores online learning approaches for detecting malicious Web sites (those involved in criminal scams) using lexical and host-based features of the associated URLs. W...
Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffr...
CVPR
2009
IEEE
1453views Computer Vision» more  CVPR 2009»
14 years 9 months ago
Learning Photometric Invariance From Diversified Color Model Ensembles
Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the i...
Jose M. Alvarez, Theo Gevers, Antonio M. Lopez
ICPR
2004
IEEE
14 years 6 months ago
Detecting Abnormal Regions in Colonoscopic Images by Patch-based Classifier Ensemble
In this paper, a new method is proposed to detect abnormal regions in colonoscopic images by patch-based classifier ensemble. Through supervised learning from image patches of var...
Kap Luk Chan, Peng Li, Shankar Muthu Krishnan, Yan...
KDD
2003
ACM
148views Data Mining» more  KDD 2003»
14 years 5 months ago
Mining concept-drifting data streams using ensemble classifiers
Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud...
Haixun Wang, Wei Fan, Philip S. Yu, Jiawei Han
KDD
2010
ACM
304views Data Mining» more  KDD 2010»
13 years 3 months ago
Automatic malware categorization using cluster ensemble
Malware categorization is an important problem in malware analysis and has attracted a lot of attention of computer security researchers and anti-malware industry recently. Todayâ...
Yanfang Ye, Tao Li, Yong Chen, Qingshan Jiang