Abstract
Meta-learning can be seen as alternating the construction of configuration of learning machines for validation, scheduling of such tasks and the meta-knowledge collection.
This article presents a few modifications of complexity measures and their application in advising to scheduling test tasks—validation tasks of learning machines in meta-learning process. Additionally some comments about their optimality in context of meta-learning are presented.
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Jankowski, N. (2013). Complexity Measures for Meta-learning and Their Optimality. In: Dowe, D.L. (eds) Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence. Lecture Notes in Computer Science, vol 7070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44958-1_15
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DOI: https://doi.org/10.1007/978-3-642-44958-1_15
Publisher Name: Springer, Berlin, Heidelberg
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