Uniform Learning of Recursive Functions

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Autor/en:
S. Zilles
Umfang:
180
EAN/ISBN:
978-3-89838-278-6
Band:
278
Ausgabe:
softcover
Buchreihe:
Dissertationen zur Künstlichen Intelligenz
Kategorien:
Buch
Informatik
Künstliche Intelligenz
Dissertationen zur Künstlichen Intelligenz
Englisch
Gesamtverzeichnis AKA Verlag
Preis:
40,00 €
inkl. 7% MwSt.
Inductive inference is concerned with formal models of learning behaviour for the analysis of general learning phenomenons. In this context, the author studies uniform inductive inference - an approach to meta-learning in the sense that, given descriptions of solvable learning problems, a meta-learner has to develop successful learners for each of them. It turns out that uniform learning is not a trivial task: the success of meta-learners strongly depends on the choice of the particular descriptions of the learning problems. The present study investigates the capabilities of meta-learners, resulting from three different formal approaches to uniform learning. The strength and weakness of uniform learning is illustrated by numerous examples; in particular, the success of specific universal methods is analysed. Finally, a formal analysis of the complexity of different learning tasks concludes the study.