Customer credit scoring is a typical class of pattern classification problem with imbalanced dataset. A new customer credit scoring method based on the support vector domain description (SVDD) classification model was proposed in this paper. Main techniques of customer credit scoring were reviewed. The SVDD model with imbalanced dataset was analyzed and the predication method of customer credit...

Spectral variability remains a challenging problem for target detection and classification in hyperspectral imagery (HSI). In this paper, we have applied the nonlinear support vector data description (SVDD) to perform full-pixel target detection. Using a pure target signature, we have developed a novel pattern recognition (PR) algorithm to train an SVDD to characterize the target class. We have...

The principal component analysis (PCA) is one of the most effective unsupervised techniques for feature extraction. To extract higher order properties of data, researchers extended PCA to kernel PCA (KPCA) by means of kernel machines. In this paper, KPCA is applied as a feature extraction procedure to dimension reduction for target detection as a preprocessing on hyperspectral images. Then the ...

Illumination change is one of most important and difficult problems which prevent from applying face recognition to real applications. For solving this, we propose a method to compensate for different illumination conditions based on SVDD(Support Vector Data Description). In the proposed method, we first consider the SVDD training for the data belonging to the facial images under various illumi...

Support vector data description (SVDD) is a well-known kernel method that constructs a minimal hypersphere regarded as a data description for a given data set. However SVDD does not take into account any statistical distribution of the data set in constructing that optimal hypersphere, and SVDD is applied to solving one-class classification problems only. This paper proposes a new approach to S...

Support vector data description (SVDD) has become a very attractive kernel method due to its good results in many novelty detection problems.Training SVDD involves solving a constrained convex quadratic programming,which requires large memory and enormous amounts of training time for large-scale data set.In this paper,we analyze the possible changes of support vector set after new samples are a...

Many high-level video processing applications such as visual surveillance require the detection and tracking of objects of interest in the video. However, due to inherent changes such as waving trees, water surfaces, ickering lights, etc., the background may not be completely static even with a xed camera. Therefore, background modeling becomes an essential and important part of such applicatio...

Support vector data description (SVDD) is a popular technique for detecting anomalies. The SVDD classifier partitions the whole space into an inlier region, which consists of the region near the training data, and an outlier region, which consists of points away from the training data. The computation of the SVDD classifier requires a kernel function, and the Gaussian kernel is a common choice ...