This volume, written by leading researchers, presents methods of
combining neural nets to improve their performance. The techniques
include ensemble-based approaches, where a variety of methods are used
to create a set of different nets trained on the same task, and modular
approaches, where a task is decomposed into simpler problems. The
techniques are also accompanied by an evaluation of their relative
effectiveness and their application to a variety of problems.