We study the potential benefits to classification prediction that arise from having access to unlabeled samples. We compare learning in the semi-supervised model to the standard, ...
We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging. The key hypothesis of multilingual learning is that by combining cues from multi...
Benjamin Snyder, Tahira Naseem, Jacob Eisenstein, ...
— Testing of graphical user interfaces is important due to its potential to reveal faults in operation and performance of the system under consideration. Most existing test appro...
Fevzi Belli, Michael Linschulte, Christof J. Budni...
Sparse representation theory has been increasingly used in the fields of signal processing and machine learning. The standard sparse models are not invariant to spatial transform...
Hidden Markov Models (HMM) are probabilistic graphical models for interdependent classification. In this paper we experiment with different ways of combining the components of an ...