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» A Support Vector Method for Clustering
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ICML
2006
IEEE
15 years 10 months ago
A continuation method for semi-supervised SVMs
Semi-Supervised Support Vector Machines (S3 VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do n...
Olivier Chapelle, Mingmin Chi, Alexander Zien
NIPS
2004
14 years 11 months ago
Maximum Margin Clustering
We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, ...
Linli Xu, James Neufeld, Bryce Larson, Dale Schuur...
OSDI
2008
ACM
15 years 10 months ago
Predicting Computer System Failures Using Support Vector Machines
Mitigating the impact of computer failure is possible if accurate failure predictions are provided. Resources, applications, and services can be scheduled around predicted failure...
Errin W. Fulp, Glenn A. Fink, Jereme N. Haack
BMCBI
2007
178views more  BMCBI 2007»
14 years 9 months ago
SVM clustering
Background: Support Vector Machines (SVMs) provide a powerful method for classification (supervised learning). Use of SVMs for clustering (unsupervised learning) is now being cons...
Stephen Winters-Hilt, Sam Merat
ICIP
2002
IEEE
15 years 11 months ago
Face detection using coarse-to-fine support vector classifiers
We describe a new face detection algorithm based on a hierarchy of support vector classifiers (SVMs) designed for efficient computation. The hierarchy serves as a platform for a c...
Hichem Sahbi, Donald Geman, Nozha Boujemaa