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Probability is more powerful than team for language identification from positive data
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Source Annual Workshop on Computational Learning Theory archive
Proceedings of the sixth annual conference on Computational learning theory table of contents
Santa Cruz, California, United States
Pages: 192 - 198  
Year of Publication: 1993
ISBN:0-89791-611-5
Authors
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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R. Freivalds. Functions computable in the limit by probabilistic machines. Mathematical Foundations of Computer Scsence, 1975.
 
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R. Freivalds. Finite identification of general recursive functions by probabilistic strategies. In Proceedings o/ the Conference on Algebraic, Arsthmetlc and Categor. scal Methods in Computation Theory, pages 138-145. Akedemie-Verlag, Berlin, 1979.
 
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R. Freivalds. On the principle capabilities of probab'distic algorithms in inductive inference. Semwtika Inform, 12:137-140, 1979.
 
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Gill. Computational complexity of probabilistic turing machines. SIAM Journal of Computing, 1977.
 
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E. M. Gold. Language identification in the limit. {nformation and Control, 10:447-474, 1967.
 
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L. Pitt. A characterization of probabilistic inference. In Proceedings of the ~5th Symposium on the Foundations of Computer Science, 1984.
 
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L. Pitt. A characterization of probabilistic inference. PhD thesis, Yale University, 1984.
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H. Rogers. G6del numberings of partial recursive functions. Journal of Symbolic Logic, 23:331-341, 1958.
 
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Collaborative Colleagues:
Sanjay Jain: colleagues
Arun Sharma: colleagues