Learning with target trajectory constraints for sequence classification tasks

The authors address the problem of designing appropriate desired (target) output signals for sequence classification tasks (such as speech recognition). Commonly the temporal evolution of the desired signals cannot be known and is (inaccurately) estimated by increasing functions such as ramps or even by don´t care´s. Here, a framework is presented to express allowed regions for the desired signals in terms of a set of trajectory inequality constraints.<>