Learning from Examples using Re ective Exploration

An important property of models constructed through the process of learning from examples is the manipulation and control of the data itself. When the data is actively selected or generated the process is known as exploration. Reeection about the internal model allows exploration to be more than just a random choice in the input space. In this paper we identify two basic forms of reeective exploration: density-based and error-based. We demonstrate the applicability of exploration processes and the advantages of using them in open systems using the task of learning a 2-dimensional continuous function.