Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Learning ML...
Markov logic networks (MLNs) use firstorder formulas to define features of Markov networks. Current MLN structure learners can only learn short clauses (4-5 literals) due to extre...
Probabilistic Computation Tree Logic (PCTL) is a wellknown modal logic which has become a standard for expressing temporal properties of finite-state Markov chains in the context...
Federico Ramponi, Debasish Chatterjee, Sean Summer...
Transfer learning addresses the problem of how to leverage knowledge acquired in a source domain to improve the accuracy and speed of learning in a related target domain. This pap...
Lilyana Mihalkova, Tuyen N. Huynh, Raymond J. Moon...
Abstract: Much has been written about word of mouth and customer behavior. Telephone call detail records provide a novel way to understand the strength of the relationship between ...