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https://www.mathworks.com/help/signal/examples/ecg-classification-using-wavelet-features.html
This example shows how to classify human electrocardiogram (ECG) signals using wavelet-based feature extraction and a support vector machine (SVM) classifier. The problem of signal classification is simplified by transforming the raw ECG signals into a much smaller set of features that serve in aggregate to differentiate different classes.
https://www.researchgate.net/publication/8345770_Wavelet_Support_Vector_Machine
Rank wavelet support vector machine (rank-WSVM) [27, 28] is proposed to apply in the classification of complex disturbances. A new method for the classification of PQ disturbances was proposed by ...
https://www.sciencedirect.com/science/article/pii/S0888327018301419
Considering the strong nonlinear dynamic characteristics of dam deformation, the prediction model of dam deformation is investigated. Support vector machine (SVM) is combined with other methods, such as phase space reconstruction, wavelet analysis and particle swarm optimization (PSO), to build the prediction model of dam deformation.Cited by: 11
https://www.sciencedirect.com/science/article/pii/S0263224119306189
To calculate the wavelet entropy of each wavelet basis in the wavelet library and determine the optimal wavelet basis as the one that achieves the minimum wavelet entropy. 2.2. Support Vector Machine. As a supervised machine learning technique, SVM proposed by Vapnik and Cortes is widely used for both classification and regression. The binary ...Cited by: 2
https://www.nature.com/articles/s41598-017-06596-z
Jul 20, 2017 · Combining Low-dimensional Wavelet Features and Support Vector Machine for Arrhythmia Beat Classification Skip to main content Thank you for visiting nature.com.Cited by: 10
https://www.researchgate.net/publication/319863437_Wavelet_Support_Vector_Machine_Algorithm_in_Power_Analysis_Attacks
Wavelet Support Vector Machine Algorithm in Power Analysis Attacks ... wavelet SVM only required an average of 5.4 traces to break the secret key for the unmasked AES implementation and less than ...
https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/s12938-015-0003-y
After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules.
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