Support Vector Machine (Based on Wavelet Kernel) for Nonlinear Curve Correction
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In this section, the author introduces a method utilizing a toolbox and Support Vector Machine (SVM) with wavelet kernel for nonlinear curve correction. This approach effectively addresses irregular curve patterns by implementing wavelet kernel functions to map input data into higher-dimensional feature spaces. A key advantage of using SVM with wavelet kernels lies in its reduced risk of overfitting through margin maximization and structural risk minimization, thereby enhancing model accuracy and reliability. However, this method presents certain limitations, such as high computational demands for large datasets and sensitivity to kernel parameter selection. When applying this technique, careful consideration of data volume and computational resources is essential, requiring optimization of parameters like penalty factor C and kernel coefficients through techniques like cross-validation or grid search algorithms.
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