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Nowadays, text classification has been one of the key subjects in intelligent information processing. Owing to the complex features of natural language, the feature space dimensions will be particularly high. How to improve the accuracy of text classification is an important and hard problem. As rough set is a useful tool to deal with uncertain information, a hybrid algorithm for text classification...
Hierarchical classification problems have been wide investigated in the past years. The available hierarchical classification methods, which use the top-down level-based scheme, often suffer from the burden of inter-level error transmission. In this paper, an instance-centric hierarchical classification framework based on decision-theoretic rough set model is proposed. The procedure of classification...
Support vector machine is a research hotspot in the area of machine learning, and the bounds on the rate of uniform convergence of statistical learning theory describe the extended ability of learning machine based on ERM. In the paper, Rough Empirical Risk Minimization (RERM) principle is proposed, and the bounds on the rate of uniform convergence of learning process with rough samples are presented...
Support Vector Machine (SVM) is a new technology of classification in data mining, which is a small sample of statistical learning theory based on structural risk minimization principle and VC theory. It has simple structure and good classification ability, but its processing speed is slow when we deal with large amount of data, affecting classification performance. In order to overcome the shortcoming...
Text categorization is an important research direction of current information retrieval. The traditional text classification method use the support vector machine (SVM) and the Bayes classification algorithm (etc). On the basis of Rough Set on text categorization, this paper put forward the idea of variable precision rough set model for Chinese text categorization, which use the attribute reduct algorithm...
In recent years, several methods on human emotion recognition have been published. But computer application on Chinese natural language processing (NLP) is still on the starting stage. In this paper, we proposed a scheme that emotion recognition from text through classification with the rough set theory and the support vector machines (SVMs). The basic steps are firstly to sample data sets, to build...
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