Slice models are collections of mathematical programs with the same structure but different data. Examples of slice models appear in Data Envelopment Analysis, where they are used to evaluate efficiency, and cross-validation, where they are used to measure generalization ability. Because they involve multiple programs, slice models tend to be data-intensive and time consuming to solve. However, by incorporating additional information in the solution process, such as the common structure and shared data, we are able to solve these models much more efficiently. In addition because of the efficiency we achieve, we are able to process much larger real-world problems and extend slice model results through the application of more computationally-intensive procedures.
Data Mining Institute Technical Report 00-10, Computer Sciences Department, University of Wisconsin, Madison, 2000