Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/99571
Title: On the power of learning robustly
Authors: Jain, Sanjay 
Smith, Carl
Wiehagen, Rolf
Issue Date: 1998
Citation: Jain, Sanjay,Smith, Carl,Wiehagen, Rolf (1998). On the power of learning robustly. Proceedings of the Annual ACM Conference on Computational Learning Theory : 187-197. ScholarBank@NUS Repository.
Abstract: A class of objects is robustly learnable if not only this class itself is learnable but all of its computable transformations do remain learnable as well. In that sense, being learnable robustly seems to be a desirable property in all fields of learning. This paper studies this phenomenon within the paradigm of inductive inference. It is shown that a more complex topological structures of the classes to be learned leads to positive robustness results, whereas an easy topological structure yields negative results. The counter-intuitive fact that even some self-referential classes can be learned robustly is also shown. Further results concerning uniformly robust learning are summarized.
Source Title: Proceedings of the Annual ACM Conference on Computational Learning Theory
URI: http://scholarbank.nus.edu.sg/handle/10635/99571
Appears in Collections:Staff Publications

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