Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-540-73001-9_39
Title: Input-dependence in function-learning
Authors: Jain, S. 
Martin, E.
Stephan, F. 
Keywords: Inductive inference
Learning with additional information
Recursion theory
Team learning
Various forms of input presentation
Issue Date: 2007
Source: Jain, S.,Martin, E.,Stephan, F. (2007). Input-dependence in function-learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4497 LNCS : 378-388. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-540-73001-9_39
Abstract: In the standard literature on inductive inference, a learner sees as input the course of values of the function to be learned. In the present work, it is investigated how reasonable this choice is and how sensitive the model is with respect to variations like the overgraph or undergraph of the function. Several implications and separations are shown and for the basic notions, a complete picture is obtained. Furthermore, relations to oracles, additional information and teams are explored. © Springer-Verlag Berlin Heidelberg 2007.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/43292
ISBN: 9783540730002
ISSN: 03029743
DOI: 10.1007/978-3-540-73001-9_39
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