SVDD

AcronymDefinition
SVDDSupport Vector Data Description
SVDDSpeed Violation Detection Deterrent (UK)
SVDDSureway Video Detection Device (speed measurement)
SVDDSoftware Version Description Document
References in periodicals archive ?
Based on the first two PCs of the training set, three types of data descriptions mentioned above, i.e., SVDD, KNNDD, and GAUSS, were constructed.
Therefore, in this subsection, we compare the performances of an unsupervised one-class SVDD classifier and a supervised binary SVM classifier.
(1) Initialize ln([C.sub.init]) and initialize [v.sub.m] to small random numbers for m = 1, ..., M; (2) while stopping criterion not met do (3) Compute C with C = exp(ln(C)); (4) Calculate [??]([x.sub.i], [x.sub.j]) with the gating model according to (7) when fixing [v.sub.m]; (5) Compute J([v.sub.m],C) by using an canonical SVM solver and an canonical SVDD solver with [??]([x.sub.i], [x.sub.j]) according to (14); (6) Compute [partial derivative](J([v.sub.m],C))/ [partial derivative][v.sub.m] for m = 1, ..., M with (20); (7) Compute [partial derivative](J([v.sub.m],C))/[partial derivative] (ln(C)) with (23); (8) Update [v.sub.m] and ln(C) by the gradient-descent method; (9) end while 3.5.
Feature vector F [F.sub.1] (6 x 6) [y x u v [u.sub.x] [u.sub.y]] [F.sub.2] (6 x 6) [y x u v [v.sub.x] [v.sub.y]] [F.sub.3] (8 x 8) [y x u v [u.sub.x] [u.sub.y] [v.sub.x] [v.sub.y]] [F.sub.4] (12 x 12) [y x u v [u.sub.x] [u.sub.y] [v.sub.x] [v.sub.y] [u.sub.xx] [u.sub.yy] [v.sub.xx] [v.sub.yy]] [F.sub.5] (17 x 17) [y x u v [u.sub.x] [u.sub.y] [v.sub.x] [v.sub.y] [u.sub.xx] [u.sub.yy] [v.sub.xx] [v.sub.yy] I [I.sub.x] [I.sub.y] [I.sub.xx] [I.sub.yy]] TABLE 2: AUC of abnormal event detection method of different features F via original SVDD which learns training samples offline, Strategy 1 online one-class SVM, and Strategy 2 online one-class SVM.
The regularization parameter C for FSVM, SVDD, FSVM-CIL, and WCS-FSVM is selected from the set {0.001, 0.01, 0.1, 1, 10, 100}.
The proposed real-time Korean native cow oestrus detection system is composed of four modules: The feature extraction and Korean native cow oestrus detection module that comprises two online process modules, and the attribute subset selection and SVDD training module of two offline process modules (Figure 1).
The approach gives firstly a point-vector decomposition to each judgment matrix using the method in preference [4], and then extract group information based on the technique for SVDD [5].
The Home Office was immediately notified and undertook tests to establish if the SVDD, when placed over the hard shoulder, could accurately record speeding vehicles in lane three.
SVDD - Speed Violation Detection Deterrent - involves sensors which are able to "read" vehicle registration plates and log the time they pass by.
In the technizues, we use the LBP [24] and SVDD (Support Vector Data Description) [25] approach for face feature extraction and learning/test, respectively.
Support vector data description (SVDD) [23] was also developed from SVM.
One-class support vector machine (OCSVM) [10] or support vector data description (SVDD) [11,12], which considers the case that training data are all normal instances, conducts a hypersphere around the normal data and utilizes the constructed hypersphere to detect an unknown sample as an inlier or outlier.