Pairwise classification and support vector machines
Advances in kernel methods
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Using decision templates to predict subcellular localization of protein
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Predicting protective bacterial antigens using random forest classifiers
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
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As more and more genomes have been discovered in recent years, it is an urgent need to develop a reliable method to predict protein subcellular localization for further function exploration. However many well-known prediction methods based on amino acid composition, have no ability to utilize the information of sequence-order. Here we propose a novel method, named moment descriptor (MD), which can obtain sequence order information in protein sequence without the need of the information of physicochemical properties of amino acids. The presented method first constructs three types of moment descriptors, and then applies multi-class SVM to the Chou's dataset. Through resubstitution, jackknife and independent tests, it is shown that the MD is better than other methods based on various types of extensions of amino acid compositions. Moreover, three multi-class SVMs show similar performance except for the training time.