The nature of statistical learning theory
The nature of statistical learning theory
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Prediction of Protein Secondary Structure with two-stage multi-class SVMs
International Journal of Data Mining and Bioinformatics
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Exploiting long-range dependencies in protein β-sheet secondary structure prediction
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
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Neural network approaches using Multi-Layer Perceptrons (MLPs) and Support Vector Machines (SVMs) for protein secondary structure prediction are presented. A two-stage SVM approach is proposed to capture the contextual relationship of secondary structure elements. The proposed technique yielded higher accuracy than the PHD (Profile network from HeiDelberg) method that cascades two MLPs. We also demonstrate that it is feasible to improve current single-stage approaches to protein secondary structure prediction by adding a second-stage prediction scheme to capture the contextual information among secondary structural elements and thereby improving the accuracy of prediction. Two-stage SVM approach achieved prediction accuracies of 72.4% and 76.7% on two databases of 126 and 513 nonhomologous globular proteins, respectively.