On polynomial-time probably almost discriminative learnability

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

On polynomial-time probably almost discriminative learnability

Full text

Pdf (766 KB)

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: 94 - 100

Year of Publication: 1993

ISBN:0-89791-611-5

Author

Kenji Yamanishi

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

Bibliometrics

Downloads (6 Weeks): 15, Downloads (12 Months): 23, Citation Count: 0

Additional Information:

references index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/168304.168316 What is a DOI?

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.

	BC91	A.R. Barron and T. Cover. Minimum complexity density estimation. IEEE Trans. on IT, 37:1034- 1054, 1991.
	BG92	M. Blum and O. Goldreich. Towards a computationM theory of statisticM tests. Proceedings of the 33rd FOCS, (pp.406-416), 1992.
	CT91	Thomas M. Cover , Joy A. Thomas, Elements of information theory, Wiley-Interscience, New York, NY, 1991
	KS90	M. Kearns and R. Schapire. Efficient distributionfree learning of probabilistic concepts. In Proceedings of IEEE the 31st FOCS, 1990.
	Ris87	J. Rissanen. Stochastic complexity. J.R. Statzst. Soc. B, 49, 3:223-239, 1987.
	RY91	J. Rissanen and B. Yu. MDL learning. Progress m Auto. and Inf. Systems, Springer Verlag.
	Riv87	Ronald L. Rivest, Learning Decision Lists, Machine Learning, v.2 n.3, p.229-246, November 1987 [doi>10.1023/A:1022607331053]
	Val84	L. G. Valiant, A theory of the learnable, Communications of the ACM, v.27 n.11, p.1134-1142, Nov. 1984 [doi>10.1145/1968.1972]
	Yam92a	Kenji Yamanishi, A Learning Criterion for Stochastic Rules, Machine Learning, v.9 n.2-3, p.165-203, July 1992 [doi>10.1007/BF00992676]
	Yam92b	Kenji Yamanishi, Probably almost discriminative learning, Proceedings of the fifth annual workshop on Computational learning theory, p.164-171, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130404]
	ZG91	O. Zeitouni and M. Gutman. On universal hypothesis testing via large deviations. IEEE Trans. on IT, 37:285-290, 1991.
	Ziv88	J. Ziv. On classification with empirically observed statistics and universa} data compression. IEEE Trans. IT, 34:278-286, 1988.