We apply a scalable approach for practical, comprehensive design space evaluation and optimization. This approach combines design space sampling and statistical inference to ident...
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
Abstract Traditional ontology mapping techniques are not strictly applicable in a dynamic and distributed environment (e.g. P2P and pervasive computing) in which on-the-fly alignm...
This paper describes our procedure and a software application for conducting large parameter sweep experiments in genetic and evolutionary computation research. Both procedure and...
Michael E. Samples, Jason M. Daida, Matthew J. Byo...
In this paper, we propose a novel approach to model shape variations. It encodes sparsity, exploits geometric redundancy, and accounts for the different degrees of local variation...