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Thousands of Machine Learning research papers contain experimental comparisons that usually have been conducted with a single focus of interest, and detailed results are usually lost after publication. Once past experiments are collected in experiment databases they allow for additional and possibly much broader investigation. In this paper, the authors show how to use such a repository to answer various interesting research questions about learning algorithms and to verify a number of recent studies.
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