Progressive rademacher sampling
Tapio Elomaa, Matti Kääriäinen · 2002
Sampling can enhance processing of large training example databases, but without knowing all of the data, or the example producing process, it is impossible to know in advance what size of a sample to choose in order to guarantee good performance. Progressive sampling has been suggested to circumvent this problem. The idea in it is to increase the sample size according to some schedule until accuracy close to that which would be obtained using all of the data is reached. How to determine this stopping time efficiently and accurately is a central difficulty in progressive sampling. We study stopping time determination...