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Document classification has received extensive attention in the past few decades due to its wide applications in many fields. To efficiently deal with this problem, a novel document classification algorithm based on information bottleneck (IB) and least square version of SVM (LS-SVM) is proposed in this paper. Extensive experimental results on the real-word document corpus show that the proposed algorithm...
Face recognition is one of the most challenging research topics in the field of pattern recognition and computer vision. To efficiently deal with this problem, a novel face recognition algorithm is proposed by using marginal manifold learning and SVM classifier. Extensive experiments show that the proposed algorithm performs much better than other well-known face recognition algorithms.
To efficiently tackle document classification problem, a novel document classification algorithm based on kernel neighborhood preserving embedding (KNPE) is proposed in this paper. The discriminant features are first extracted by the KNPE algorithm, then SVM is used to classify the documents into semantically different classes. Experimental results on real document databases have demonstrated the...
To efficiently cope with document classification problem, an efficient document classification algorithm based on local discriminant embedding (LDE) and SVM classifier is proposed in this paper. The high-dimensional document space are first projected into the lower-dimensional feature space by using LDE algorithm, the SVM classifier is then applied in the reduced document feature space. Extensive...
Text categorization aims to assign text documents to predefined categories. In this paper, a novel text categorization algorithm that combines the LDA and SVM is proposed. The core idea of the algorithm is as follows: The high dimension text data set are first projected into a lower-dimensional text subspace. Then the SVM classifier algorithm is applied to classify the text. Experimental results on...
Document categorization is one of the most crucial techniques to organize the documents in a supervised manner. To efficiently resolve document classification problems, a novel document classification algorithm based on kernel discriminant analysis (KDA) is proposed in this paper. The high-dimensional document sets are first mapped into lower-dimensional space with KDA, then the SVM is applied to...
Text categorization is the process of assigning documents to a set of previously fixed categories. In this paper we develop an optimal SVM algorithm for text classification via multiple optimal strategies, such as a novel importance weight definition, the feature selection using the likelihood ratio for binomial distribution, the optimal parameter settings, etc. Comparison between our method and other...
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