What is a common solution for preventing polynomial overfitting in SVMs?

Using a low-degree polynomial kernel is a common solution for preventing polynomial overfitting in Support Vector Machines (SVMs). When employing polynomial kernels, higher degrees can lead to models that capture too much noise in the training data, resulting in overfitting. By opting for a lower-degree polynomial kernel, the model maintains a simpler structure, which can generalize better to unseen data. This effectively balances the bias-variance trade-off, as a low-degree polynomial kernel can still represent complex relationships in the data without becoming overly complex.

In contrast, simply selecting a linear kernel might not be suitable for data that has a nonlinear relationship, limiting the model's ability to capture essential patterns. Applying data normalization is a good practice for improving model performance but does not directly address the issue of polynomial complexity leading to overfitting. Reducing the dimensionality of data is another technique that can be helpful in mitigating overfitting by simplifying the feature space, but it does not specifically target the polynomial nature of the kernel being used in SVMs. Thus, choosing a low-degree polynomial kernel stands out as a targeted strategy to prevent overfitting in this context.