Markov logic networks (MLNs) combine first-order logic and Markov networks, allowing us to handle the complexity and uncertainty of real-world problems in a single consistent fram...
Real-world data -- especially when generated by distributed measurement infrastructures such as sensor networks -- tends to be incomplete, imprecise, and erroneous, making it impo...
Abstract— Automatic pattern classifiers that allow for incremental learning can adapt internal class models efficiently in response to new information, without having to retrai...
Abstract— Making inferences and choosing appropriate responses based on incomplete, uncertainty and noisy data is challenging in financial settings particularly in bankruptcy de...
With the enormous and still growing amount of data and user interaction on the Web, it becomes more and more necessary for data consumers to be able to assess the trustworthiness ...