I have a an assignment to make a classifier for hand-written numbers with a limited data set of 500 samples.I am currently using python, I tried sklearn SVC with linear classifier and I got an accuracy of about 80%. I also tried KNN with 13 neighbors and got a max of 81% accuracy.I need to get to at least 90% accuracy. What are your suggestions that would help with training (adding noise? what distribution? / tilting the pictures with 90 degrees? Maybe cross validating over different parameters... etc).Edit:To clarify the main challenge:I have to reach an accuracy of more that 90% with only using 500 samples, I can play around with the samples however I want to extend the data but I am not allowed to use external samples.
Question about handwritten numbers recognition with limited data set
machine learning;classification