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This paper compares the performance of linear and nonlinear kernels of Support Vector Machines (SVM) used for text classification. The study is motivated by the previous viewpoint that linear SVM performs better than nonlinear one, and that, although there are many investigations have proved that SVM performs well in text classification, there is no serious investigation on the comparison between...
In e-commerce transactions, goods are classified according to the hierarchical structure, which refers to a tree category. In the process of classification, we shall consider the special features. While using brand name for category, for instance, the degree of distinction characteristic of brand is higher. Based on this, we prepare a dictionary of brands for Chinese words segamentatin on one hand...
Automatic Web site classification has a wide application prospect. However, there is a little research on the Web site classification. Many methods represent the Web site as normal text and still use the methods of text classification. But Web sites are combination of many Web pages via hyperlinks, so the methods of text classification are not suitable for Web sites. This paper proposes a new approach...
Internet is becoming a spreading platform for the public opinion. It is important to grasp the Internet public opinion in time and understand the trends of their opinion correctly. Text classification plays a fundamental role in a number of information management and retrieval tasks. But Web-page classification is much more difficult than pure-text classification due to a large variety of noisy information...
For multi-view learning, existing methods usually exploit originally provided features for classifier training, which ignore the latent correlation between different views. In this paper, semantic features integrating information from multiple views are extracted for pattern representation. Canonical correlation analysis is used to learn the representation of semantic spaces where semantic features...
Most semi-supervised learning methods assume there are a number of labeled data available in order to learn a classifier which then exploits a large set of unlabeled data. However, for some applications, there are only extremely spare labeled examples attainable (say, one example per category). In this case, these semi-supervised learning methods can not work. In this paper, a new method for seeking...
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