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Document feature extraction and classifier selection are two key problems for document classification approach. To effectively resolve the above two problems, a novel document classification algorithm is proposed by combining the merits of local fisher discriminant analysis and kernel logistic regression. Extensive experiments have been conducted, and the results demonstrate that the proposed algorithm...
To cope with performance and accuracy problems with high dimensionality in document classification, a novel dimensionality reduction algorithm called IKDA is proposed in this paper. The proposed IKDA algorithm combines kernel-based learning techniques and direct iterative optimization procedure to deal with the nonlinearity of the document distribution. The proposed algorithm also effectively solves...
Dimensionality reduction algorithms, which try to reduce the dimensionality of data and to enhance the discriminant information, are of paramount importance in document classification. In this paper, a novel dimensionality reduction algorithm called orthogonal locality discriminant embedding (OLDE) is proposed to address these problems. The OLDE algorithm effectively combines the idea of local discriminant...
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 deal with document classification problem, an efficient document classification algorithm based on kernel local discriminant embedding (kernel LDE) is proposed in this paper. The high-dimensional document data are first mapped into lower-dimensional feature space, then the SVM classifier is applied to classify documents. The experimental results demonstrate that the proposed algorithm...
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...
The goal of a text classification system is to determine whether a given document belongs to which of the predefined categories. An optimal SVM algorithm for text classification via multiple optimal strategies is proposed in this paper. The experimental results indicate that the proposed optimal classification algorithm yields much better performance than other conventional algorithms
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