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Learning from the past with experiment databases

Abstract
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, we show how to use such a repository to answer various interesting research questions about learning algorithms and to verify a number of recent studies. Alongside performing elaborate comparisons and rankings of algorithms, we also investigate the effects of algorithm parameters and data properties, and study the learning curves and bias-variance profiles of algorithms to gain deeper insights into their behavior.
Type
Working Paper
Type of thesis
Series
Computer Science Working Papers
Citation
Vanschoren, J., Pfahringer, B. & Holmes, G. (2008). Learning from the past with experiment databases. (Working paper 08/2008). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
Date
2008-06-24
Publisher
University of Waikato, Department of Computer Science
Degree
Supervisors
Rights