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Shanghai, China

Li L.-Q.,Chongqing Medical University | Zhang Y.,Chongqing Medical University | Zou L.-Y.,Chongqing Medical University | Zhou Y.,Chongqing Medical University | And 2 more authors.
Protein and Peptide Letters

Many proteins bear multi-locational characteristics, and this phenomenon is closely related to biological function. However, most of the existing methods can only deal with single-location proteins. Therefore, an automatic and reliable ensemble classifier for protein subcellular multi-localization is needed. We propose a new ensemble classifier combining the KNN (K-nearest neighbour) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic, Gram-negative bacterial and viral proteins based on the general form of Chou's pseudo amino acid composition, i.e., GO (gene ontology) annotations, dipeptide composition and AmPseAAC (Amphiphilic pseudo amino acid composition). This ensemble classifier was developed by fusing many basic individual classifiers through a voting system. The overall prediction accuracies obtained by the KNN-SVM ensemble classifier are 95.22, 93.47 and 80.72% for the eukaryotic, Gram-negative bacterial and viral proteins, respectively. Our prediction accuracies are significantly higher than those by previous methods and reveal that our strategy better predicts subcellular locations of multi-location proteins. © 2012 Bentham Science Publishers. Source

Peng X.,Shanghai Normal University | Peng X.,Shanghai Universities
Pattern Recognition

A novel twin parametric-margin support vector machine (TPMSVM) for classification is proposed in this paper. This TPMSVM, in the spirit of the twin support vector machine (TWSVM), determines indirectly the separating hyperplane through a pair of nonparallel parametric-margin hyperplanes solved by two smaller sized support vector machine (SVM)-type problems. Similar to the parametric-margin νsupport vector machine (par-νSVM), this TPMSVM is suitable for many cases, especially when the data has heteroscedastic error structure, that is, the noise strongly depends on the input value. But there is an advantage in the learning speed compared with the par-νSVM. The experimental results on several artificial and benchmark datasets indicate that the TPMSVM not only obtains fast learning speed, but also shows good generalization. © 2011 Elsevier Ltd. All rights reserved. Source

Peng X.,Shanghai Normal University | Peng X.,Shanghai Universities
Neural Networks

The learning speed of classical Support Vector Regression (SVR) is low, since it is constructed based on the minimization of a convex quadratic function subject to the pair groups of linear inequality constraints for all training samples. In this paper we propose Twin Support Vector Regression (TSVR), a novel regressor that determines a pair of ε{lunate}-insensitive up- and down-bound functions by solving two related SVM-type problems, each of which is smaller than that in a classical SVR. The TSVR formulation is in the spirit of Twin Support Vector Machine (TSVM) via two nonparallel planes. The experimental results on several artificial and benchmark datasets indicate that the proposed TSVR is not only fast, but also shows good generalization performance. © 2009 Elsevier Ltd. All rights reserved. Source

Peng X.,Shanghai Normal University | Peng X.,Shanghai Universities
Information Sciences

In this paper, a ν-twin support vector machine (ν-TSVM) is presented, improving upon the recently proposed twin support vector machine (TSVM). This ν-TSVM introduces a pair of parameters (ν) to control the bounds of the fractions of the support vectors and the error margins. The theoretical analysis shows that this ν-TSVM can be interpreted as a pair of minimum generalized Mahalanobis-norm problems on two reduced convex hulls (RCHs). Based on the well-known Gilbert's algorithm, a geometric algorithm for TSVM (GA-TSVM) and its probabilistic speed-up version, named PGA-TSVM, are presented. Computational results on several synthetic as well as benchmark datasets demonstrate the significant advantages of the proposed algorithms in terms of both computation complexity and classification accuracy. © 2010 Elsevier Inc. All rights reserved. Source

Guo B.-Y.,Shanghai Normal University | Guo B.-Y.,Shanghai Universities | Wang T.-J.,Henan University of Science and Technology
Journal of Scientific Computing

In this paper, we investigate composite Laguerre-Legendre spectral method for fourth-order exterior problems. Some results on composite Laguerre-Legendre approximation are established, which is a set of piecewise mixed approximations coupled with domain decomposition. These results play an important role in spectral method for fourth-order exterior problems with rectangle obstacle. As examples of applications, composite spectral schemes are provided for two model problems, with convergence analysis. Efficient algorithms are implemented. Numerical results demonstrate their high accuracy, and confirm theoretical analysis well. © Springer Science+Business Media, LLC 2010. Source

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