learning (semi-supervised classification) or to incorporate prior information such as
class labels, pairwise constraints or cluster membership (semi-supervised
clustering). Active learning or selective sampling (Settles 2009) refers to methods
where the learning algorithm has control on the data selection, e.g. it can select the
most important/informative examples from a pool of unlabeled examples, then a
human expert is asked for the correct data label. Here is the aim is to reduce
annotation costs. In our application - the recognition of human emotions in human
computer interaction - we focus more on active lear ...