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» Data Mining via Support Vector Machines
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ICDM
2006
IEEE
108views Data Mining» more  ICDM 2006»
15 years 3 months ago
Minimum Enclosing Spheres Formulations for Support Vector Ordinal Regression
We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. B...
Shirish Krishnaj Shevade, Wei Chu
MCS
2005
Springer
15 years 3 months ago
Half-Against-Half Multi-class Support Vector Machines
A Half-Against-Half (HAH) multi-class SVM is proposed in this paper. Unlike the commonly used One-Against-All (OVA) and One-Against-One (OVO) implementation methods, HAH is built ...
Hansheng Lei, Venu Govindaraju
NIPS
2000
14 years 11 months ago
Feature Selection for SVMs
We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This sear...
Jason Weston, Sayan Mukherjee, Olivier Chapelle, M...
ICPR
2010
IEEE
15 years 1 months ago
Malware Detection on Mobile Devices Using Distributed Machine Learning
This paper presents a distributed Support Vector Machine (SVM) algorithm in order to detect malicious software (malware) on a network of mobile devices. The light-weight system mo...
Ashkan Sharifi Shamili, Christian Bauckhage, Tansu...
ICAC
2006
IEEE
15 years 3 months ago
Fast and Effective Worm Fingerprinting via Machine Learning
— As Internet worms become ever faster and more sophisticated, it is important to be able to extract worm signatures in an accurate and timely manner. In this paper, we apply mac...
Stewart M. Yang, Jianping Song, Harish Rajamani, T...