The effort necessary to construct labeled sets of examples in a supervised learning scenario is often disregarded, though in many applications, it is a time-consuming and expensi...
In many real world applications, active selection of training examples can significantly reduce the number of labelled training examples to learn a classification function. Differ...
This paper describes an application of active learning methods to the classification of phone strings recognized using unsupervised phonotactic models. The only training data req...
Distance functions are an important component in many learning applications. However, the correct function is context dependent, therefore it is advantageous to learn a distance f...
Discriminative approaches to human pose inference involve mapping visual observations to articulated body configurations. Current probabilistic approaches to learn this mapping ha...