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» Data Mining via Support Vector Machines
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KDD
2003
ACM
129views Data Mining» more  KDD 2003»
16 years 4 months ago
Online novelty detection on temporal sequences
: Novelty detection, or anomaly detection, on temporal sequences has increasingly attracted attention from researchers in different areas. In this paper, we present a new framework...
Junshui Ma, Simon Perkins
ICC
2007
IEEE
141views Communications» more  ICC 2007»
15 years 10 months ago
A Hybrid Model to Detect Malicious Executables
— We present a hybrid data mining approach to detect malicious executables. In this approach we identify important features of the malicious and benign executables. These feature...
Mohammad M. Masud, Latifur Khan, Bhavani M. Thurai...
156
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AUSAI
2008
Springer
15 years 6 months ago
Learning to Find Relevant Biological Articles without Negative Training Examples
Classifiers are traditionally learned using sets of positive and negative training examples. However, often a classifier is required, but for training only an incomplete set of pos...
Keith Noto, Milton H. Saier Jr., Charles Elkan
ICIAR
2005
Springer
15 years 10 months ago
On the Individuality of the Iris Biometric
We consider quantitatively establishing the discriminative power of iris biometric data. It is difficult, however, to establish that any biometric modality is capable of distingui...
Sungsoo Yoon, Seung-Seok Choi, Sung-Hyuk Cha, Yill...
ICML
2009
IEEE
16 years 5 months ago
Group lasso with overlap and graph lasso
We propose a new penalty function which, when used as regularization for empirical risk minimization procedures, leads to sparse estimators. The support of the sparse vector is ty...
Laurent Jacob, Guillaume Obozinski, Jean-Philippe ...