Support vector machines are very robust to overfitting, and in our experiments we find that classification results were quite robust to feature vectors with up to 4 times the size of the training set.
modern machine learning techniques like support vector machines allow us to explore large feature vectors, combining these different feature sets to achieve high classification accuracy in style-based tasks.