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Incidents of public security have an ascendant trend in recent years all over the world, and it is more important to understand the correlation of different kinds of public security incidents. With the popularization of the Internet, numerous web messages can provide resources to do that. However, an important challenge is that the web messages are often heterogeneous and unstructured. In this paper,...
Reviews on Web can help small investors make decision in selecting funds. The size of fund reviews is smaller than other products, which proposes a challenge to extract sentiment by using statistic methods. We develop a methodology to deal with this problem by using association rule to select seed words and introducing new outside resources to improve the traditional PMI performance. The result shows...
With the proliferation of online media services, ad video has become an important way to promote various products, services and ideas. Research efforts have been devoted to the contextual advertising whereas comprehensive recommendation of video ads is less exploited. In this paper, we propose to establish a semantic linking between video ads and relevant product/service online in a cross-media manner...
Latent semantic indexing (LSI) is an effective technique for feature extraction in text mining, and supervised LSI (SLSI) algorithms have been proposed to exploit the class labels of training data. In this paper, we propose an iterative SLSI framework based on class selection. We show that a previous iterative SLSI algorithm is an instance of the framework. We also propose a method under our framework,...
In the research of software reuse, feature models have been widely adopted to organize the requirements of a set of applications in a software domain. However, there still lacks an effective approach to minimizing analysts' participation in feature models' construction. In this paper, we propose a use case based semi-automatic approach to the construction of feature models. The basic idea of this...
With the proliferation of online media services, video ads are pervasive across various platforms involving Internet services and interactive TV services. Existing research efforts such as Google AdSense and MSRA videosense/imagesense have been devoted to the less intrusive insertion of relevant textual or video ads in streams or Web pages through text/image/video content analysis whereas the inherent...
Nowadays, various learning technologies are required on uncertain data. As an important pre-processing step in data mining, feature selection needs to consider this vagueness or uncertainty. In this paper, we propose a novel algorithm to evaluate the correlation between features and uncertain class labels on the basis of Hilbert-Schmidt Independence Criterion. Consequently, the features can be ranked...
Feature selection is among the keys in many applications, especially in mining high-dimensional data. With lack of labeled instances, the learning accuracy may deteriorate using traditional methods. In this paper, we introduce a ldquowrapperrdquo type semi-supervised feature selection approach based on RSC model. It extends the class label from labeled training set to unlabeled data. Additionally,...
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