The nature of statistical learning theory
The nature of statistical learning theory
Outlier detection and localisation with wavelet based multifractal formalism
Outlier detection and localisation with wavelet based multifractal formalism
Removing divergences in the negative moments of the multi-fractal partition function with the wavelet transformation
Segmentation of multispectral remote sensing images using active support vector machines
Pattern Recognition Letters
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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We propose a novel hybrid Hölder-SVM detection algorithm for arrhythmia classification. The Hölder exponents are computed efficiently using the wavelet transform modulus maxima (WTMM) method. The hybrid system performance is evaluated using the benchmark MIT-BIH arrhythmia database. The implemented model classifies 160 of Normal sinus rhythm, 25 of Ventricular bigeminy, 155 of Atrial fibrillation and 146 of Nodal (A-V junctional) rhythm with 96.94% accuracy. The distinct scaling properties of different types of heart rhythms may be of clinical importance.