14 research outputs found
Replicative potentials of various fusion products between WI-38 and SV40 transformed WI-38 cells and their components
Replicative potential of individual cell hybrids derived from young and old donor human skin fibroblasts
Intensification of insecticide resistance in UK field populations of the peach-potato aphid, Myzus persicae
Phenotype of low proliferative potential is dominant in hybrids of normal human fibroblasts
In Online Structure Learning for Markov Logic Networks
Abstract. Most existing learning methods for Markov Logic Networks (MLNs) use batch training, which becomes computationally expensive and eventually infeasible for large datasets with thousands of training examples which may not even all fit in main memory. To address this issue, previous work has used online learning to train MLNs. However, they all assume that the model’s structure (set of logical clauses) is given, and only learn the model’s parameters. However, the input structure is usually incomplete, so it should also be updated. In this work, we present OSL—the first algorithm that performs both online structure and parameter learning for MLNs. Experimental results on two realworld datasets for natural-language field segmentation show that OSL outperforms systems that cannot revise structure.