Advances in Affective and Pleasurable Design

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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 ...