SVM Implementation for Data Classification

In data classification tasks, Support Vector Machine (SVM) serves as a widely-used algorithm that employs kernel functions to transform data into higher-dimensional spaces for effective separation. The implementation allows users to select appropriate kernel functions (linear, polynomial, radial basis function, or sigmoid) based on specific image segmentation requirements. The system automatically trains optimal classification hyperplanes using quadratic programming optimization, maximizing the margin between different classes while minimizing classification errors. Through SVM's implementation, developers can achieve precise data classification results with built-in parameters for further optimization and adjustment, including cost parameter tuning and kernel-specific parameter configuration for enhanced model performance. The algorithm effectively handles both linear and non-linear classification scenarios through its kernel trick implementation.