This paper addresses the supervised learning in which the class membership of training data are subject to uncertainty. This problem is tackled in the framework of the Dempster-Sha...
Recent advances in semantic image analysis have brought forth generic methodologies to support concept learning at large scale. The attained performance however is highly variable,...
Stamatia Dasiopoulou, Ioannis Kompatsiaris, Michae...
We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in paramet...
Developer testing is a type of testing where developers test their code as they write it, as opposed to testing done by a separate quality assurance organization. Developer testin...
Tao Xie, Jonathan de Halleux, Nikolai Tillmann, Wo...
—This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available o...