About choosing the hyper-parameters about osdn HOT 1 CLOSED

Emailed the author. He replied as follows.

Hi Xiangyi Yan,

Thanks for your interest. For ALPHA_RANK, I tried few iterations and rank 10 seemed to be performing best across categories. For WEIBULL_TAIL_SIZE it is a bit tricky. More specifically it depends on the total size of data you have for fitting. For imagenet, each category has about 700-1000 images. Hence if you consider tail or a quantile of distritbuion it is about 70-100 images. However, weibull distributions are more effective in predicting extreme events, thats what you need even smaller tail-sizes to harness power of the distribution. Thats why I considered even smaller tail sizes. Very small tailsize (less than 10) dont give enough data for fitting. Too large tail sizes (more than 40 with data distribution of 1000) dont give enough variability.

Abhijit

from osdn.