Adaptive Random Testing subsumes a class of algorithms that detect the first failure with less test cases than Random Testing. The present paper shows that a "reference metho...
We propose a model-based learning algorithm, the Adaptive Aggregation Algorithm (AAA), that aims to solve the online, continuous state space reinforcement learning problem in a de...
Automatic video search based on semantic concept detectors has recently received significant attention. Since the number of available detectors is much smaller than the size of h...
A fundamental assumption for any machine learning task is to have training and test data instances drawn from the same distribution while having a sufficiently large number of tra...
We present Pro3Gres, a deep-syntactic, fast dependency parser that combines a handwritten competence grammar with probabilistic performance disambiguation and that has been used i...
Gerold Schneider, Kaarel Kaljurand, Fabio Rinaldi,...