Abstract
Learning theory is an active research area with contributions from various fields including artificial intelligence, theoretical computer science, and statistics. The main thrust is an attempt to model learning phenomena in precise ways and study the mathematical properties of these scenarios. In this way one hopes to get a better understanding of the learning scenarios and what is possible or as we call it learnable in each. Of course this goes with a study of algorithms that achieve the required performance. Learning theory aims to define reasonable models of phenomena and find provably successful algorithms within each such model. To complete the picture we also seek impossibility results showing that certain things are not learnable within a particular model, irrespective of the particular learning algorithms or methods being employed.
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Abe, N., Khardon, R., Zeugmann, T. (2001). Editors’ Introduction. In: Abe, N., Khardon, R., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2001. Lecture Notes in Computer Science(), vol 2225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45583-3_1
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DOI: https://doi.org/10.1007/3-540-45583-3_1
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