I found some slides [1] explaining how this works.
A less poetic example of privileged information: if you're training on time-series information, you can include events from the future in the training examples, even though they won't be available while making predictions in production.
Apparently this helps the machine learning algorithm find the outlying data points when the data isn't linearly separable.
A less poetic example of privileged information: if you're training on time-series information, you can include events from the future in the training examples, even though they won't be available while making predictions in production.
Apparently this helps the machine learning algorithm find the outlying data points when the data isn't linearly separable.
[1] http://web.mit.edu/zoya/www/SVM+.pdf