Computational learning theory: Difference between revisions

Revision as of 10:03, 8 January 2004

Revision as of 10:28, 8 January 2004

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* [Angluin, 92] Angluin, D. 1992. Computational learning theory: Survey and selected bibliography. In Proceedings of the Twenty-Fourth Annual ACM Symposium on Theory of Computing (May 1992), pp. 351--369.

* [Hau,90] D. Haussler. Probably approximately correct learning. In AAAI-90 Proceedings of the Eight National Conference on Artificial Intelligence, Boston, MA, pages 1101--1108. American Association for Artificial Intelligence, 1990.

http://citeseer.nj.nec.com/haussler90probably.html

Feature selection

* [DH,94] A. Dhagat and L. Hellerstein. PAC learning with irrelevant attributes. In Proceedings of the IEEE Symp. on Foundation of Computer Science, 1994. To appear.

http://citeseer.nj.nec.com/dhagat94pac.html

Inductive inference

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Optimal O notation learning

* [GG96] O. Goldreich, D. Ron. On universal learning algorithms.

http://citeseer.nj.nec.com/69804.html

Negative results

* [KV,89] M. Kearns and L. G. Valiant. 1989. Cryptographic limitations on learning boolean formulae and finite automata. In Proceedings of the 21st Annual ACM Symposium on Theory of Computing, pages 433--444, New York. ACM.

http://citeseer.ist.psu.edu/kearns89cryptographic.html

[[Boosting]]

* [Sch, 90] Robert E. Schapire. The strength of weak learnability. Machine Learning, 5(2):197--227, 1990

http://citeseer.nj.nec.com/schapire90strength.html

[[Probably approximately correct learning]]