Abstract
Benchmark experiments produce data in a very specific format. The observations are drawn from the performance distributions of the candidate algorithms on resampled data sets. In this paper we introduce a comprehensive toolbox of exploratory and inferential analysis methods for benchmark experiments based on one or more data sets. We present new visualization techniques, show how formal non-parametric and parametric test procedures can be used to evaluate the results, and, finally, how to sum up to a statistically correct overall order of the candidate algorithms.
Dokumententyp: | Paper |
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Keywords: | benchmark experiment, learning algorithm, visualisation, inference, ranking |
Fakultät: | Mathematik, Informatik und Statistik > Statistik > Technische Reports |
URN: | urn:nbn:de:bvb:19-epub-4134-6 |
Sprache: | Englisch |
Dokumenten ID: | 4134 |
Datum der Veröffentlichung auf Open Access LMU: | 02. Jun. 2008, 07:44 |
Letzte Änderungen: | 04. Nov. 2020, 12:47 |