Proceedings of the ICML/UAI/COLT Joint Planning to Learn Workshop (PlanLearn08) edition:2nd pages:10-15
ICML/UAI/COLT Joint workshop day location:Helsinki date:9 July 2008
Many studies in machine learning try to investigate what makes an algorithm succeed or fail on certain datasets. However, the field is still evolving relatively quickly, and new algorithms, preprocessing methods, learning tasks and evaluation procedures continue to emerge in the literature. Thus, it is impossible for a single study to cover this expanding space of learning approaches. In this paper, we propose a community-based approach for the analysis of learning algorithms, driven by sharing meta-data from previous experiments in a uniform way. We illustrate how organizing this information in a central database can create a practical public platform for any kind of exploitation of meta-knowledge, allowing effective reuse of previous experimentation and targeted analysis of the collected results.