University of Konstanz
Graduiertenkolleg / PhD Program
Computer and Information Science

Graduation Talks


Explorative adaptive active classification


Nicolas Cebron, University Konstanz
Konstanz, Germany

date & place

Wednesday, 04.07.2007, 17:00 h
Room C252


Classifying large datasets without any a-priori information poses a problem in numerous tasks. Especially in industrial environments, we often encounter diverse measurement devices and sensors that produce huge amounts of data, but we still rely on a human expert to help give the data a meaningful interpretation. As the amount of data that must be manually classified plays a critical role, we need to reduce the number of learning episodes involving human interactions as much as possible. In addition for real world applications it is fundamental to converge in a stable manner to a solution that is close to the optimal solution. This thesis discusses novel techniques to perform active learning on a large unlabeled dataset. The focus of our work lies on introducing the concept of exploration (finding representative examples) in active learning methods. Other active learning methods ignore the prior data distribution and are trained on a set of initially random sampled examples. We try to overcome this limitation with two different techniques and compare them to other active learning approaches.