The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Feature extraction from Doppler ultrasound signals for automated diagnostic systems
Computers in Biology and Medicine
A novel large-memory neural network as an aid in medical diagnosis applications
IEEE Transactions on Information Technology in Biomedicine
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Combined neural networks for diagnosis of erythemato-squamous diseases
Expert Systems with Applications: An International Journal
Automated diagnostic systems: arterial disorders detection
ACS'08 Proceedings of the 8th conference on Applied computer scince
Implementation of automated diagnostic systems: ophthalmic arterial disorders detection case
International Journal of Systems Science
| Hi-index | 12.05 |
In this paper, the multiclass support vector machines (SVMs) with the error correcting output codes (ECOC) were presented for the multiclass Doppler ultrasound signals (ophthalmic arterial Doppler signals and internal carotid arterial Doppler 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 present research demonstrated that the wavelet coefficients are the features which well represent the studied Doppler ultrasound signals and the multiclass SVMs trained on these features achieved high classification accuracies.