Organizing the world's machine learning information

Abstract

All around the globe, thousands of learning experiments are being executed on a daily basis, only to be discarded after interpretation. Yet, the information contained in these experiments might have uses beyond their original intent and, if properly stored, could be of great use to future research. In this paper, we hope to stimulate the development of such learning experiment repositories by providing a bird's-eye view of how they can be created and used in practice, bringing together existing approaches and new ideas. We draw parallels between how experiments are being curated in other sciences, and consecutively discuss how both the empirical and theoretical details of learning experiments can be expressed, organized and made universally accessible. Finally, we discuss a range of possible services such a resource can offer, either used directly or integrated into data mining tools. © 2008 Springer-Verlag.