Adaptive Sampling technique for selecting negative examples for artificial intelligence applications

摘要

Artificial intelligence applications require the use of training sets, including positive and negative examples. Negative examples are selected using distributions of positive examples relating to the dominant feature in the feature space. Negative examples can share or roughly share positive examples, which are values of a basic feature within feature space. This type of training set is shown in relation to content recommenders, especially recommenders for television shows.