However, with the exception of speech recognition systems, the methods that have been applied have generally been ad hoc. The learning algorithms used to train the "base classifier" (i.e., the classifier applied to the individual pieces) are not designed for the divide-and-conquer setting, and the "merge" methods are typically not trained at all.
A goal of our research is to develop learning algorithms for the "base classifier" and "merge" steps that can be applied to a wide variety of divide-and-conquer learning algorithms. We wish to develop a language for specifying divide-and-conquer problems and a toolbox for constructing divide-and-conquer solutions. Our analysis of this problem is described in Machine Learning for Sequential Data: A Review.