A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
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Machine Learning
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Meaningful MRA intitialization for discrete time series
Signal Processing - Special issue on current topics in adaptive filtering for hands-free acoustic communication and beyond
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Nonlinear Biomedical Signal Processing: Fuzzy Logic, Neural Networks, and New Algorithms
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Pattern Classification (2nd Edition)
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ISM '05 Proceedings of the Seventh IEEE International Symposium on Multimedia
Computers in Biology and Medicine
Laryngeal pathology detection by means of class-specific neural maps
IEEE Transactions on Information Technology in Biomedicine
Wavelet analysis of long-range-dependent traffic
IEEE Transactions on Information Theory
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Detecting novel hypermethylated genes in Breast cancer benefiting from feature selection
Computers in Biology and Medicine
Wavelet adaptation for automatic voice disorders sorting
Computers in Biology and Medicine
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Unilateral vocal fold paralysis, vocal fold polyp, and vocal fold nodules are the most common types of neurogenic and organic vocal disorders. This article aims to distinguish these types of vocal diseases into four different classes for the purpose of automatic screening. Firstly, the reconstructed signal at each wavelet packet decomposition sub-band in five levels of decomposition with mother wavelet of (db10) is used to extract the nonlinear features of self-similarity and approximate entropy. Also, wavelet packet coefficients are used to measure energy and Shannon entropy features at different spectral sub-bands. Consequently, to find a discriminant feature vector, three different methods have been applied: Davies-Bouldin (DB) criteria, genetic algorithm (GA) with the fitness functions of support vector machine's (SVM) and k-nearest neighbor's (KNN) recognition rates. Finally, obtained feature vectors have been passed on to SVM and KNN classifiers. The results show that a feature vector of length 12 obtained by the optimization method of GA with the fitness function of SVM's recognition rate fed to SVM classifier achieves the highest classification accuracy of 91%. Furthermore, nonlinear features play an important role in pathological voice classification by participating rate of approximately 67% in the optimal feature vector.