Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Forecasting Skewed Biased Stochastic Ozone Days: Analyses and Solutions
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
ACM Computing Surveys (CSUR)
Ensembles of One Class Support Vector Machines
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Evaluating Learning Algorithms: A Classification Perspective
Evaluating Learning Algorithms: A Classification Perspective
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Anomaly detection using One-Class Support Vector Machine (OCSVM) have attracted wide attention in practical applications. Recent research focuses on enhancing OCSVM using either ensemble learning techniques or Multiple Kernel Learning (MKL) since single kernels such as the Gaussian Radial-Based Function (GRBF) kernel might not be flexible enough to construct a proper feature space. In this paper, we develop a new kernel, called centralized GRBF. Further, the two GRBF and centralized GRBF are combined by using a new ensemble kernel technique, called Coupled Ensemble-Kernels (CEK), to improve OCSVM for anomaly detection. Therefore, the final classification model is itself a large-margin classifier while it is actually an ensemble classifier coined with two sub-large-margin models. We show that the proposed CEK outperforms previous approaches using traditional ensemble learning methods and MKL for anomaly detection.