Distance metric learning and nonlinear dimensionality reduction are two interesting and active topics in recent years. However, the connection between them is not thoroughly studi...
In this paper, a systematic study of the effects of complexity of prediction methodology on its accuracy for a set of real applications on a variety of HPC systems is performed. R...
Laura Carrington, Michael Laurenzano, Allan Snavel...
A local cell quality metric is introduced and used to construct a variational functional for a grid smoothing algorithm. A maximum principle is proved and the properties of the loc...
Abstract. The K2 metric is a well-known evaluation measure (or scoring function) for learning Bayesian networks from data [7]. It is derived by assuming uniform prior distributions...
Graph layout algorithms typically conform to one or more aesthetic criteria (e.g. minimising the number of bends, maximising orthogonality). Determining the extent to which a grap...