We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. ...
When no training or adaptation data is available, semisupervised training is a good alternative for processing new domains. We perform Bayesian training of a part-of-speech (POS) ...
In this work we present Cutting Plane Inference (CPI), a Maximum A Posteriori (MAP) inference method for Statistical Relational Learning. Framed in terms of Markov Logic and inspi...
For effective retrieval of visual information, statistical learning plays a pivotal role. Statistical learning in such a context faces at least two major mathematical challenges: ...
Mobile Ambients (MA) have acquired a fundamental role in modelling mobility in systems with mobile code and mobile devices, and in computation over administrative domains. We pres...