I'm new in machine learning and i want to study how to use random ferns.
I read this paper Fast Keypoint Recognition in Ten Lines of Code and implement simple version of algorithm.
But then I tried to run this algorithm on MNIST dataset (without any pre-processing) I achieved only 7.5% error rate. I created 40 ferns and 40 pairs of points for each fern. I chose random subset of training data to train each fern (it improves error rate a little)
I suppose that 7.5% is not really bad result. But i was able to achieve 3% by simple Random Forest (CART)
So my question is: How i can improve this error rate and is 7.5% ok for Random Ferns? I do not want to obtain cool digits classifier but i want to know is my implementation works well and how to improve Random Ferns.