Abstract. In supervised learning, discretization of the continuous explanatory attributes enhances the accuracy of decision tree induction algorithms and naive Bayes classifier. M...
When labeled examples are limited and difficult to obtain, transfer learning employs knowledge from a source domain to improve learning accuracy in the target domain. However, the...
ErHeng Zhong, Wei Fan, Jing Peng, Kun Zhang, Jiang...
Abstract. Probabilistic timed automata are an extension of timed automata with discrete probability distributions. Simulation and bisimulation relations are widely-studied in the c...
Although classical first-order logic is the de facto standard logical foundation for artificial intelligence, the lack of a built-in, semantically grounded capability for reasonin...
This work presents a formal probabilistic approach for solving optimization problems in design automation. Prediction accuracy is very low especially at high levels of design flo...