Language learning in dependence on the space of hypotheses

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Language learning in dependence on the space of hypotheses

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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: 127 - 136

Year of Publication: 1993

ISBN:0-89791-611-5

Authors

Steffen Lange
Thomas Zeugmann

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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Downloads (6 Weeks): 14, Downloads (12 Months): 28, Citation Count: 17

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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.

	1	ADLEMAN, L.M., AND BLUM,M. (1991), Inductive inference and unsolvability, Journal of Symbolic Logic 56,891 - 900.
	2	ANGLUIN, D. (1980), Inductive inference of formal languages from positive data, Information and Control45, 117- 135.
	3	Martin Anthony , Norman Biggs, Computational learning theory: an introduction, Cambridge University Press, New York, NY, 1992
	4	FREIVALDS, R., KINBER, E.B., AND WmHA- OF.N, R. (1988), Prohabilistic versus deterministic inductive inference in nonstandard numberings. Zeiischrift f. Mathematische Logik and Grnndlagen der Mathematik 34 531 - 539.
	5	FI~EIVALDS, R., KINBEIt, E.B., AND WmHAGEN, R. (1992), Convergently versus divergently incorrect hypotheses in inductive inference, GOSLER Report 02/92, January 1992, Fachbereich Mathematik und Informatik, TH Leipzig.
	6	GOLD, E.M. (1967), Language identification in the limit, Information and Control 10,447 - 474.
	7	Klaus P. Jantke, Monotonic and non-monotonic inductive inference, New Generation Computing, v.8 n.4, p.349-360, 1991 [doi>10.1007/BF03037092]
	8	Shyam Kapur, Monotonic Language Learning, Proceedings of the Third Workshop on Algorithmic Learning Theory, p.147-158, October 20-22, 1992
	9	Shyam Kapur , Gianfranco Bilardi, Language Learning without Overgeneralization, Proceedings of the 9th Annual Symposium on Theoretical Aspects of Computer Science, p.245-256, February 13-15, 1992
	10	KZNBER, E.B. (1993), personal communication.
	11	Steffen Lange , Thomas Zeugmann, Types of monotonic language learning and their characterization, Proceedings of the fifth annual workshop on Computational learning theory, p.377-390, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130427]
	12	Steffen Lange , Thomas Zeugmann, Monotonic Versus Nonmonotonic Language Learning, Proceedings of the Second International Workshop on Nonmonotonic and Inductive Logic, p.254-269, December 02-06, 1991
	13	Steffen Lange , Thomas Zeugmann, Language Learning with a Bounded Number of Mind Changes, Proceedings of the 10th Annual Symposium on Theoretical Aspects of Computer Science, p.682-691, February 25-27, 1993
	14	LANCE, S., AND ZEUGMANN, T. (1993c), Learning recursive languages with bounded mind changes, International Journal of Foundations of Computer Science, to appear.
	15	LANGV., S., AND ZEUGMANN, T. (1993d), The learnability of recursive languages in dependence on the hypothesis space, GOSLER-Report 20/93, FB Mathematik und Informatik, TH Leipzig.
	16	LANGg, S., Zv. UCMANN, T., AND KAPUR, S. (1992), Class preserving monotonic language learning, submitted to Theoretical Computer Science, and GOSLER-Report 14/92, FB Mathematik und Informatik, TH Leipzig.
	17	Michael Machtey , Paul Young, An Introduction to the General Theory of Algorithms, Elsevier Science Inc., New York, NY, 1978
	18	Yasuhito Mukouchi, Characterization of Finite Identification, Proceedings of the International Workshop on Analogical and Inductive Inference, p.260-267, October 05-09, 1992
	19	Yasuhito Mukouchi, Inductive Inference with Bounded Mind Changes, Proceedings of the Third Workshop on Algorithmic Learning Theory, p.125-134, October 20-22, 1992
	20	Balas Kausik Natarajan, Machine learning: a theoretical approach, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991
	21	Daniel N. Osherson , Michael Stob , Scott Weinstein, Systems that learn: an introduction to learning theory for cognitive and computer scientists, MIT Press, Cambridge, MA, 1986
	22	SHINOHARA, T. (1986), Studies on inductive inference from positive data, PhD thesis, Kyushu University, Japan.
	23	Takeshi Shinohara, Inductive inference from positive data is powerful, Proceedings of the third annual workshop on Computational learning theory, p.97-110, August 06-08, 1990, Rochester, New York, United States
	24	Rolf Wiehagen, A Thesis in Inductive Inference, Proceedings of the 1st International Workshop on Nonmonotonic and Inductive Logic, p.184-207, December 04-07, 1990

CITED BY 17

	Efim Kinber , Frank Stephan, Language learning from texts (extended abstract): mind changes, limited memory and monotonicity, Proceedings of the eighth annual conference on Computational learning theory, p.182-189, July 05-08, 1995, Santa Cruz, California, United States
	Ganesh R. Baliga , John Case , Sanjay Jain, Synthesizing enumeration techniques for language learning, Proceedings of the ninth annual conference on Computational learning theory, p.169-180, June 28-July 01, 1996, Desenzano del Garda, Italy
	Arun Sharma , Frank Stephan , Yuri Ventsov, Generalized notions of mind change complexity, Proceedings of the tenth annual conference on Computational learning theory, p.96-108, July 06-09, 1997, Nashville, Tennessee, United States
	Steffen Lange , Gunter Grieser, Variants of iterative learning, Theoretical Computer Science, v.292 n.2, p.359-376, 27 January 2003
	Susanne Kaufmann , Frank Stephan, Resource bounded next value and explanatory identification: learning automata, patterns and polynomials on-line, Proceedings of the tenth annual conference on Computational learning theory, p.263-274, July 06-09, 1997, Nashville, Tennessee, United States
	Léa Meyer, Aspects of complexity of conservative probabilistic learning, Proceedings of the eleventh annual conference on Computational learning theory, p.72-78, July 24-26, 1998, Madison, Wisconsin, United States
	Steffen Lange, The representation of recursive languages and its impact on the efficiency of learning, Proceedings of the seventh annual conference on Computational learning theory, p.256-267, July 12-15, 1994, New Brunswick, New Jersey, United States
	Steffen Lange , Gunter Grieser, On the power of incremental learning, Theoretical Computer Science, v.288 n.2, p.277-307, 16 October 2002
	Arun Sharma , Frank Stephan , Yuri Ventsov, Generalized notions of mind change complexity, Information and Computation, v.189 n.2, p.235-262, March 15, 2004
	Steffen Lange , Sandra Zilles, Relations between gold-style learning and query learning, Information and Computation, v.203 n.2, p.211-237, December 15, 2005
	La Meyer, Aspects of complexity of probabilistic learning under monotonicity constraints, Theoretical Computer Science, v.268 n.2, p.275-322, 17 October 2001
	Steffen Lange , Thomas Zeugmann , Sandra Zilles, Learning indexed families of recursive languages from positive data: A survey, Theoretical Computer Science, v.397 n.1-3, p.194-232, May, 2008
	Steffen Lange , Gunter Grieser , Thomas Zeugmann, Inductive inference of approximations for recursive concepts, Theoretical Computer Science, v.348 n.1, p.15-40, 2 December 2005
	Thomas Zeugmann, From learning in the limit to stochastic finite learning, Theoretical Computer Science, v.364 n.1, p.77-97, 2 November 2006
	Sanjay Jain , Steffen Lange , Sandra Zilles, A general comparison of language learning from examples and from queries, Theoretical Computer Science, v.387 n.1, p.51-66, November, 2007
	Sanjay Jain , Frank Stephan , Ye Nan, Prescribed Learning of Indexed Families, Fundamenta Informaticae, v.83 n.1-2, p.159-175, August 2008
	Sanjay Jain , Frank Stephan , Nan Ye, Prescribed learning of r.e. classes, Theoretical Computer Science, v.410 n.19, p.1796-1806, April, 2009