Because Support Vector Data Description (SVDD) fails in identifying the local geometric information, a new detection method, called One-class SVDD based on Local Patch (OCSVDD_LP), was proposed. First, the data was divided into many local patches. Then, each sample was reconstructed by using the corresponding local patch. Finally, the decision model was obtained through training on the reconstruction data with SVDD. The experimental results on the artificial data set demonstrate that OCSVDD_LP can not only capture the global geometric structure of the data set, but also uncover the local geometric information. Besides, the results on real-world data sets validate the effectiveness of the proposed method.

Since fuzzy rough sets induced by fuzzy equivalence relations can not quite accurately reflect decision problems described by numerical attributes among fuzzy concept domain, a fuzzy rough set model based on neighborhood relation called NR-FRS was proposed. First of all, the definitions of the rough set model were presented. Based on properties of NR-FRS, a fuzzy neighborhood approximation space reasoning was carried out, and attribute dependency in characteristic subspace was also analyzed. Finally, feature selection algorithm based on NR-FRS was presented, and feature subsets was constructed next, which made fuzzy positive region greater than a specific threshold, thereby getting rid of redundant features and reserving attributes that have a strong capability in classification. Classification experiment was implemented on UCI standard data sets, which used Radial Basis Function (RBF) support vector machine as the classifier. The experimental results show that, compared with fast forward feature selection based on neighborhood rough set as well as Kernel Principal Component Analysis (KPCA), feature number of the subset obtained by NR-FRS model feature selection algorithm changes more smoothly and stably according to parameters. Meanwhile, average classification accuracy increases by 5.2% in the best case and varies stably according to parameters.

Concerning the low training speed of mammography multi-classification, the Hypersphere Multi-Class Support Vector Data Description (HSMC-SVDD) algorithm was proposed. The Hypersphere One-Class SVDD (HSOC-SVDD) was extended to a HSMC-SVDD as a kind of immediate multi-classification. Through extracting gray-level co-occurrence matrix features of mammography, then Kernel Principle Component Analysis (KPCA) was used to reduce dimension, finally HSMC-SVDD was used for classification. As each category trained only one HSOC-SVDD, its training speed was higher than that of the present multi-class classifiers. The experimental results show that compared with the combined classifier, in which the average train time is 40.2 seconds, proposed by Wei (WEI L Y, YANG Y Y, NISHIKAWA R M,et al.A study on several machine-learning methods for classification of malignant and benign clustered micro-calcifications. IEEE Transactions on Medical Imaging, 2005, 24(3): 371-380), the training time of HSMC-SVDD classifier is 21.369 seconds, the accuracy is up to 76.6929% and it is suitable for solving classification problems of many categories.

The existing antivirus methods take too much system overhead, consume a lot of network bandwidth and can not detect the unknown programs in time. Therefore, this paper improved the previous work and presented a new virtual desktop antivirus model regarding virtual desktop infrastructure. It supported active antivirus and passive antivirus moves. Privileged virtual machines were used to scan viruses, manage the trust-list and transmit signatures of every virtual machine to others. Agents were used to analyze the signatures and characteristics of files, optimize the bytes to be uploaded and scanned, and scan the programs timely when being loaded. The experimental results show that model can detect viruses in real-time, in the meantime reduce system overhead and network bandwidth usage.

This paper proposed a new, real-time and robust local feature and descriptor, which can be applied to computer vision field with high demands in real-time. Since CenSurE has extremely efficient computation,it has got wide attention. Due to its linear scales, the filter response signal is very sparse and cannot acquire high repeatability. Therefore, this paper modified the detector using logarithmic scale sampling, and obtained better performance. The new rapid descriptor was based on gradient of the summed image patch, called GSIP. The GSIP descriptor has superior performance. An extensive experimental evaluation was performed to show that the GSIP descriptor increases the distinctiveness of local image descriptors for image region matching and object recognition compared with the state-of-the-art SURF descriptor. Furthermore, compared with SURF, GSIP achieves a two-fold speed increase.

Based on the computational economy model of resource management in grid computing, a credit model was presented in which the resource credit, GSP credit, GSC credit were being defined. A Grid Credit Architecture(GCA) was being designed to meet the model and then discussed the policy in credit evaluating. As to implementation problems, the deploy of credit module and Extended Resource Usage Record (ERUR) was discussed in detail. Emulation experiments show that the new architecture is efficient and valuable within grid computing economy environments.