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With the wide adoption of social networks, people are accustomed to post their ideas and thinking via these platforms. Tweets or comments online usually come with individual sentiment, which are time consuming to be analyzed by human labor. This study encapsulates a prototype Chinese sentiment mining system and takes a global hotel reviewing website TripAdvisor as the evaluation sample. The proposed...
This paper provides a novel technique for multiple kernel learning within Support Vector Machine framework. The problem of combining different sources of information arises in several situations, for instance, the classification of data with asymmetric similarity matrices or the construction of an optimal classifier from a collection of kernels. Often, each source of information can be expressed as...
Learning from positive and unlabeled examples (PU learning) has been a hot topic for classification in machine learning. The key feature of this problem is that there is no labeled negative training data, which makes the traditional classification techniques inapplicable. According to this feature, we propose an algorithm called biased locality-sensitive support vector machine based on density (BLSBD-SVM)...
A novel method, namely ensemble support vector machine with segmentation (SeEn–SVM), for the classification of imbalanced datasets is proposed in this paper. In particular, vector quantization algorithm is used to segment the majority class and hence generates some small datasets that are of less imbalance than original one, and two different weighted functions are proposed to integrate all the results...
The importance of network-based approach to identifying biological markers has been increasingly recognized. Lots of papers indicated that genes in a network tend to function together in biological processes, so taking full advantage of the biological observation can improve the performance of microarray classification. However, lots of SVM methods don't consider this situation during their classifier...
Support vector machines (SVM) is a classification technique based on the structural risk minimization principle. It is characteristic of processing complex data and high accuracy. And the ensemble of classifiers often has better performance than any of component classifiers in the ensemble. In this paper, bagging, boosting, multiple SVM decision model (MSDM) and heterogeneous SVM decision model (HSDM)...
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