Support vector domain description

By David Martinus Johannes Tax, Robert P. W. Duin. 1999

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Citations in the corpus

Security analysis of online centroid anomaly detection (2010, methodology): “Hence several direct methods have been proposed for anomaly detection, e.g., oneclass SVM (Schölkopf et al., 2001), support vector data description (SVDD) (Tax and Duin, 1999a,b), and density level set estimation (Polonik, 1995; Tsybakov, 1997; Steinwart et al., 2005).”
A systematic study of the class imbalance problem in convolutional neural networks (2017, methodology): “They include Support Vector Data Description (SVDD) method [67, 68], hyper-plane learning that separates outliers [69], neighbourhood based OSVM [70], Support Vector Mapping Convergence [71], or fuzzy OSVM [72, 73], just to mention some.”
Learning Cumulatively to Become More Knowledgeable (2016, background): “SVDD tries to place a hypersphere with the minimum radius around almost all the positive training points.”
Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection (2008, background): “The purpose is to find a minimumvolume hypersphere in a high-dimensional feature space H, with radius R > 0 and center a ∈ H, which contains most of these data objects [33] [Fig.”
A Survey of Recent Trends in One Class Classification (2009, background): “Tax and Duin [11][12] and Scholkopf et al.”
Core Vector Machines: Fast SVM Training on Very Large Data Sets (2005, background): “Another heuristic approach for scaling up the soft-marginSVDD using core sets has also been proposed in (Chu et al., 2004).”
A tutorial overview of anomaly detection in hyperspectral images (2010, methodology): “is used. The SVDD [ 77 , 78] is a one-class clas sifier”
PEBL: Web page classification without negative examples (2004, background): “Linear kernels are fast, but generally Gaussian kernels perform better in terms of accuracy, especially for single-class classification problems [27].”
Kernel Methods and Machine Learning (2014, methodology): “The principle behind the hypersphere one-class SVM (hypersphere OCSVM) proposed by Tax and Duin [267, 268] is illustrated by Figure 11.”
Sensor-Based Abnormal Human-Activity Detection (2008, methodology): “For this purpose, we employ the one-class SVMs [24] .”
A survey of kernel and spectral methods for clustering (2008, background): “This approach provides a support vector description in feature space [74,77,78].”