Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Support Vector Data Description
Machine Learning
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
IEEE Transactions on Neural Networks
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Support Vector Data Description (SVDD) concerns the characterization of a data set. A good description covers all target data but includes no superfluous space. The boundary of a data set can be used to detect outliers. SVDD is affected by noises during being trained. In this paper, Grid-based Fuzzy Support Vector Data Description (G-FSVDD) is presented to deal with the problem. G-FSVDD reduces the effects of noises by a new fuzzy membership model, which is based on grids. Each grid is a hypercube in data set. After obtaining enough grids, Apriori algorithm is used to find grids with high density. In G-FSVDD, different training data make different contributions to the domain description according to their density. The advantage of G-FSVDD is shown in the experiment.