The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Neural Computing and Applications
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A modified mixture of experts network structure for ECG beats classification with diverse features
Engineering Applications of Artificial Intelligence
A novel large-memory neural network as an aid in medical diagnosis applications
IEEE Transactions on Information Technology in Biomedicine
Input feature selection for classification problems
IEEE Transactions on Neural Networks
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In this paper, the multiclass support vector machine (SVM) with the error correcting output codes (ECOC) was presented for the multiclass time-varying biomedical signals (electrocardiogram signals) classification problems. Decision making was performed in two stages: feature extraction by computing the wavelet coefficients and classification using the classifier trained on the extracted features. The purpose was to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The research demonstrated that the wavelet coefficients are the features which well represent the studied time-varying biomedical signals and the multiclass SVMs trained on these features achieved high classification accuracies.