A Comparison of Robust Model Choice Criteria Within a Metalearning Study

Author:

Vidnerová Petra,Kalina Jan,Güney Yeşim

Publisher

Springer International Publishing

Reference44 articles.

1. Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: Petrov, B., Csaki, F. (eds.) Second International Symposium on Information Theory, pp. 267–281. Budapest, Academiai Kaido (1973)

2. Borra, S., Di Ciaccio, A.: Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods. Comput. Statist. Data Anal. 54, 2976–2989 (2010)

3. Brazdil, P., Giraud-Carrier, C., Soares, C., Vilalta, E.: Metalearning: Applications to Data Mining. Springer, Berlin (2009)

4. Brazdil, P., Giraud-Carrier, C.: Metalearning and algorithm selection: progress, state of the art and introduction to the 2018 special issue. Mach. Learn. 107, 1–14 (2018)

5. California housing dataset. https://github.com/ageron/handson-ml/tree/master/datasets/housing (2019)

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