Support Vector Machine Regression Fitting - Concrete Compressive Strength Prediction

(1) SVM is specifically designed for small sample problems, capable of obtaining optimal solutions with limited data samples; implementation involves kernel function selection and parameter optimization; (2) The SVM algorithm ultimately transforms into a quadratic programming problem that theoretically achieves global optimal solutions, contrasting with traditional neural networks' local optimum issues; the optimization process utilizes Lagrange multipliers and sequential minimal optimization (SMO) methods; (3) SVM's topological structure is determined by support vectors, avoiding the trial-and-error network structure determination required in traditional neural networks; this is implemented through support vector identification algorithms that automatically determine the model complexity.