COLT (COmputational Learning Theory) is a field of machine learning.
In COLT the main interest is the computational aspects of learning, e.g., the time complexity of learning problem. The computational aspects are considered in a learning framework, like the very common one of Probably approximately correct learning.
Note that a computation is considered feasible if it can be done in polynomial time.
There are two kind of results in COLT. Possitive results - Showing the a certain class of function is learnable in polynomial time. Negative results - Showing that certain classes cannot be learned in polynomial time. Negative results were proven only by assumption. The assumptions the are common in negative results are: Computational - P<>NP Cryptographic - One way functions exits.
These are good surveys of COLT:
• [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.
This is a good paper on 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.
A list of important COLT papers
[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. [Gold, 67] E. M. Gold. Language identification in the limit. Information and Control, 10:447--474, 1967. [GG96] O. Goldreich, D. Ron. On universal learning algorithms. [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. [Sch, 90] Robert E. Schapire. The strength of weak learnability. Machine Learning, 5(2):197--227, 1990 [Valiant, 84] L. Valiant. A Theory of the Learnable. Communications of the ACM, 27(11):1134--1142, 1984.
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