Utilization of large disordered sample sets for classifier adaptation in complex domains

A methodology for structuring large disordered sample sets for classifiers is presented. The object-oriented framework is an essential part of this methodology. Classes can be viewed as sets, and sets again can be viewed as objects. For this reason, operations and techniques from both domains (sets and OO technology) can be utilized to set up a system for computer-aided labeling. Since labeling is a time-consuming task, the handling of the system has to support efficient labeling. A second important aspect of the application is easy system handling to allow inexperienced examiners to use the system