However, for the case of non-linear kernels, anchored and L2-SVM produce different results, as the anchoring is applied prior to the kernel expansion.
for the case of the second-degree polynomial kernel, L2-SVM aims to maximize: In our experiments, as discussed in Section 5.4, we find that anchored-SVM and soft-margin SVM with tuned C value both reach good results when we reduce the amount of lexical features.