The experiment involved the implementation of both primal and dual formulations of SVM on three different datasets: a generated dataset, a banknote authentication dataset, and a linearly nonseparable dataset. The performance of the SVM models was evaluated based on the accuracy, objective function values, weights, and the number of support vector points.
In summary, the SVM models showed promising performance on the datasets analyzed. The choice of formulation and regularization parameter significantly influenced the accuracy, the objective function value, the weights, and the number of support vector points. The results demonstrate the effectiveness of SVM in classification tasks and the importance of parameter tuning in achieving optimal performance.