Support vector data description (SVDD) is a widely used novelty detection algorithm. It provides excellent predictions even in the absence of negative samples and retains the mathematical elegance of Support Vector Machines. The decision boundary can be very flexible due to the incorporation of kernel...
Support vector data description As a one-class classi?cation method, the goal of support vector data description (SVDD) is to ?nd the minimum hyper-sphere that can enclose most of normal (target) data in the feature space. Given the target dataset X = {x1 , x2 , . . . , xl }, ...
Improved multiclass support vector data description for planetary gearbox fault diagnosis 2021, Control Engineering Practice Citation Excerpt : Fault diagnosis methods for planetary gearbox are mainly divided into three classes, i.e. model-based methods, signal processing methods and data-driven methods ...
FitOrderOrders of the functions used to fit the scaling data in this iteration, returned as a two-element vector. The two entries are the fit order for theDandGscalings, respectively. The default command-window display shows these values in theFit Ordercolumn. ...
Local illumination models compute the radiance from local properties—such as the position, normal vector, and material data—in addition to global light source parameters. Each point is shaded independently, which opens up a lot of possibilities for parallel computation. This indepen...
DESCRIPTION OF PREFERRED EMBODIMENTS OF THE INVENTION FIG. 1 shows an eye/gaze tracking apparatus according to a first embodiment of the invention. The eye/gaze tracking apparatus includes an image registering unit110, a control unit120and a data processing unit130. ...
Description musyndesigns a robust controller for an uncertain plant using D-K iteration, which combinesH∞synthesis (K step) withμanalysis (D step) to optimize closed-loop robust performance. You can usemusynto: Synthesize "black box" unstructured robust controllers. ...
S2). These observations suggest that, after appropriate transformations, a Gaussian mixture model may be a reasonable description of the data. We assumed that the components of the mixture model represent “QC cell types,” where cells at the centers of the distributions are good quality, and ...
3. Data description Although our suggested techniques are generally applicable, the focus application in this work is related to maintenance planning for railway track infrastructure. The latter forms an assembly of multiple components (rails, sleepers, ballast, switches, etc.), as illustrated in Fig...
Whole data*(Pred-MutHTP): all mutations in human transmembrane proteins are considered. 2.2. Feature representation and selection In this work, each mutation was represented by a feature vector in a multi-dimensional information space. Herein, we extracted four different types of features, including...