Multi-sphere Support Vector Data Description for Outliers Detection on Multi-distribution Data
SVDD has been proved a powerful tool for outlier detection. However, in detecting outliers on multi-distribution data, namely there are distinctive distributions in the data, it is very challenging for SVDD to generate a hyper-sphere for distinguishing outliers from normal data. Even if such a hyper-sphere can be identified, its performance is usually not good enough. This paper proposes an multi-sphere SVDD approach, named MS-SVDD, for outlier detection on multi-distribution data. First, an adaptive sphere detection method is proposed to detect data distributions in the dataset. The data is partitioned in terms of the identified data distributions, and the corresponding SVDD classifiers are constructed separately. Substantial experiments on both artificial and real-world datasets have demonstrated that the proposed approach outperforms original SVDD.

title: Multi-Space-Mapped SVMs for Multi-class Classification
abstract: In SVMs-based multiple classification, it is not always possible to find an appropriate kernel function to map all the classes from different distribution functions into a feature space where they are linearly separable from each other. This is even worse if the number of classes is very large. As a result, the classification accuracy is not as good as expected. In order to improve the performance of SVMs-based multi-classifiers, this paper proposes a method, named multi-space-mapped SVMs, to map the classes into different feature spaces and then classify them. The proposed method reduces the requirements for the kernel function. Substantial experiments have been conducted on one-against-all, one-against-one, FSVM, DDAG algorithms and our algorithm using six UCI data sets. The statistical results show that the proposed method has a higher probability of finding appropriate kernel functions than traditional methods and outperforms others.

title: Nesting Algorithm for Multi-Classification Problems
abstract: Support vector machines (SVMs) are originally designed for binary classifications. As for multi-classifications, they are usually converted into binary ones. In the conventional multi-classifiable algorithms, One-against-One algorithm is a very power method. However, there exists a middle unclassifiable region. In order to overcome this drawback, a novel method called Nesting Algorithm is presented in this paper. Our ideas are as follows: firstly, construct the optimal hyperplanes based on One-against-One approach. Secondly, if there exist data points in the middle unclassifiable region, select them to construct the optimal hyperplanes with the same hyperparameters. Thirdly, repeat the second step until there are no data points in the unclassifiable region or the region is disappeared. In this paper, we also prove the validity of the proposed algorithm for unclassifiable region and give the computational complexity analysis of the method. In order to examine the training accuracy and the generalization performance of the proposed algorithm, One-against-One algorithm, fuzzy least square support vector machine (FLS-SVM) and the proposed algorithm are applied to five UCI datasets. The results show that the training accuracy of the proposed algorithm is higher than the others, and its generalization performance is also comparable with them.

title: Fully Automatic Text Categorization by Exploiting WordNet
abstract: This paper proposes a Fully Automatic Categorization approach for Text (FACT) by exploiting the semantic features from WordNet and document clustering. In FACT, the training data is constructed automatically by using the knowledge of the category name. With the support of WordNet, it first uses the category name to generate a set of features for the corresponding category. Then, a set of documents is labeled according to such features. To reduce the possible bias originating from the category name and generated features, document clustering is used to refine the quality of initial labeling. The training data are subsequently constructed to train the discriminative classifier. The empirical experiments show that the best performance of FACT can achieve more than 90% of the baseline SVM classifiers in F1 measure, which demonstrates the effectiveness of the proposed approach.

title: Classification using support vector machines with graded resolution
abstract: A method which we call support vector machine with graded resolution (SVM-GR) is proposed in this paper. During the training of the SVM-GR, we first form data granules to train the SVM-GR and remove those data granules that are not support vectors. We then use the remaining training samples to train the SVM-GR. Compared with the traditional SVM, our SVM-GR algorithm requires fewer training samples and support vectors, hence the computational time and memory requirements for the SVM-GR are much smaller than those of a conventional SVM that use the entire dataset. Experiments on benchmark data sets show that the generalization performance of the SVM-GR is comparable to the traditional SVM.

title: Mining Opinion Features in Customer Reviews
abstract: It is a common practice that merchants selling products on the Web ask their customers to review the products and associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds. This makes it difficult for a potential customer to read them in order to make a decision on whether to buy the product. In this project, we aim to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we are only interested in the specific features of the product that customers have opinions on and also whether the opinions are positive or negative. We do not summarize the reviews by selecting or rewriting a subset of the original sentences from the reviews to capture their main points as in the classic text summarization. In this paper, we only focus on mining opinion/product features that the reviewers have commented on. A number of techniques are presented to mine such features. Our experimental results show that these techniques are highly effective.

title: REGULARIZED SENSE RECONSTRUCTION USING ITERATIVELY REFINED TOTAL VARIATION METHOD
abstract: SENSE has been widely accepted as one of the standard reconstruction algorithms for parallel MRI. When large acceleration factors are employed, the SENSE reconstruction becomes very ill-conditioned. For Cartesian SENSE, Tikhonov regularization has been commonly used. However, the Tikhonov regularized image usually tends to be overly smooth, and a high-quality regularization image is desirable to alleviate this problem but is not available. In this paper, we propose a new SENSE regularization technique that is based on total variation with iterated refinement using Bregman iteration. It penalizes highly oscillatory noise but allows sharp edges in reconstruction without the need for prior information. In addition, the Bregman iteration refines the image details iteratively. The method is shown to be able to significantly reduce the artifacts in SENSE reconstruction

title: Dealing with different distributions in learning from
abstract: In the problem of learning with positive and unlabeled examples, existing research all assumes that positive examples P and the hidden positive examples in the unlabeled set U are generated from the same distribution. This assumption may be violated in practice. In such cases, existing methods perform poorly. This paper proposes a novel technique A-EM to deal with the problem. Experimental results with product page classification demonstrate the effectiveness of the proposed technique.