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Face recognition has become one of the most important research areas of pattern recognition and machine learning due to its potential applications in many fields. To effectively cope with this problem, a novel face recognition algorithm is proposed by using manifold learning and minimax probability machine. Comprehensive comparisons and extensive experiments show that the proposed algorithm achieves...
Document classification has attracted increasing attention recently as a result of the ever-increasing amounts of document data on the Internet. In this paper, an efficient document classification algorithm is proposed by combining the ideas of maximum margin criterion (MMC) and minimax probability machine (MPM). Experimental results on three well-known benchmark document datasets demonstrate the...
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...
Document classification has received extensive attention in the past decade due to its wide range applications. To efficiently deal with this problem, a novel document classification algorithm is proposed by using marginal fisher analysis (MFA) and minimax probability machine(MPM). Experimental results on the WebKB data set show that the proposed algorithm achieves much better performance than other...
To efficiently deal with the curse of dimensionality in the content-based image retrieval (CBIR) system, a novel image retrieval algorithm is proposed by combination of local discriminant embedding (LDE) and least square SVM (LS-SVM) in this paper. LDE aims to achieve good discriminating performance by integrating the local geometrical structure and class relations between image data. LS-SVM classifier...
Classification rule mining has been a very active research topic in data mining and machine learning communities. To effectively cope with this problem, a novel classification rule mining algorithm is proposed by the combination of neighborhood preserving embedding (NPE) and genetic algorithm (GA) in this paper. Experimental results on the UCI data set repository demonstrate that the proposed algorithm...
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 deal with spam mail filtering problem, a novel spam filtering algorithm based on locality pursuit projection (LPP) and least square version of SVM(LS-SVM) is proposed in this paper. The mail message features are first extracted by the LPP algorithm, then the LS-SVM classifier is used to classify mails into spam and legitimate. Experimental results demonstrate that the proposed algorithm...
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...
With the explosive increase in document data on the Internet, classifying documents from document database has become one of the hottest research fields.To efficiently deal with this problem, an efficient document classification algorithm based on kernel locality preserving projection (Kernel LPP) is presented in this paper. Experimental results show that the proposed algorithm outperforms other related...
Document categorization is one of the most crucial techniques to assign the documents of a corpus to a set of previously fixed categories. To efficiently deal with document categorization problem, an efficient document categorization algorithm based on local discriminant embedding (LDE) and memetic algorithm (MA) is proposed in this paper. Extensive experiments on Reuter-21578 demonstrate that the...
With many potential applications in document management and Web searching, document classification has recently gained more attention. To efficiently resolve this problem, an efficient document classification algorithm based on neighborhood preserving embedding (NPE) and particle swarm optimization (PSO) is proposed in this paper. The document features are first extracted by the NPE algorithm, then...
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...
Face recognition has received growing attention because of its wide applications. In this paper, an efficient face recognition algorithm based on non-negative matrix factorization (NMF) and SVM is proposed. The high dimension face images are first projected into a lower-dimensional subspace using NMF. The SVM classifier is then used to classify the face image into different classes. The experimental...
With the explosive growth in the Web documents, classifying document from the large-scale document database has become one of the most active research fields in data mining communities. Thus, developing an efficient document categorization algorithm to automatically classify Web document is of great importance. In this paper, an efficient document classification algorithm with shuffled frog leaping...
Classification rule mining is one of the important problems in the field of data mining which aims to extract a small set of rules from the training data set with predetermined targets. In this paper, an efficient classification rule mining algorithm is proposed by using memetic algorithm (MA). Experimental results show that the proposed classification algorithm achieves much better performance than...
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...
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