The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Multi-Level Approach to SCOP Fold Recognition
BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
Research on classification methods of glycoside hydrolases mechanism
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds
Computers in Biology and Medicine
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Fold recognition based on sequence-derived features is a complex multi-class classification problem. In the current study, we comparatively assess five different classification techniques, namely multilayer perceptron and probabilistic neural networks, nearest neighbour classifiers, multi-class support vector machines and classification trees for fold recognition on a reference set of proteins that are organised in 27 folds and are described by 125-dimensional vectors of sequence-derived features. We evaluate all classifiers in terms of total accuracy, mutual information coefficient, sensitivity and specificity measurements using a ten-fold cross-validation method. A polynomial support vector machine and a multilayer perceptron of one hidden layer of 88 nodes performed better and achieved satisfactory multi-class classification accuracies (42.8% and 42.1%, respectively) given the complexity of the problem and the reported similar classification performances of other researchers.