Abstract
The present work investigates the relationship of iterative learning with other learning criteria such as decisiveness, caution, reliability, non-U-shapedness, monotonicity, strong monotonicity and conservativeness. Building on the result of Case and Moelius that iterative learners can be made non-U-shaped, we show that they also can be made cautious and decisive. Furthermore, we obtain various special results with respect to one-one texts, fat texts and one-one hypothesis spaces.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Angluin, D.: Inductive inference of formal languages from positive data. Information and Control 45, 117–135 (1980)
Blum, L., Blum, M.: Toward a mathematical theory of inductive inference. Information and Control 28, 125–155 (1975)
Baliga, G., Case, J., Merkle, W., Stephan, F., Wiehagen, R.: When unlearning helps. Information and Computation 206, 694–709 (2008)
Case, J.: Periodicity in generations of automata. Mathematical Systems Theory 8, 15–32 (1974)
Case, J.: Infinitary self-reference in learning theory. Journal of Experimental and Theoretical Artificial Intelligence 6, 3–16 (1994)
Case, J.: The power of vacillation in language learning. SIAM Journal on Computing 28, 1941–1969 (1999)
Case, J., Kötzing, T.: Strongly non-U-shaped learning results by general techniques. In: Proceedings of COLT (Conference on Computational Learning Theory), pp. 181–193 (2010)
Case, J., Lynes, C.: Machine inductive inference and language identification. In: Proceedings of ICALP (International Colloquium on Automata, Languages and Programming), pp. 107–115 (1982)
Case, J., Moelius III, S.E.: Optimal language learning. In: Freund, Y., Györfi, L., Turán, G., Zeugmann, T. (eds.) ALT 2008. LNCS (LNAI), vol. 5254, pp. 419–433. Springer, Heidelberg (2008)
Case, J., Moelius, S.E.: U-shaped, iterative, and iterative-with-counter learning. Machine Learning 72, 63–88 (2008)
Fulk, M.: Prudence and other conditions on formal language learning. Information and Computation 85, 1–11 (1990)
Mark Gold, E.: Language identification in the limit. Information and Control 10, 447–474 (1967)
Grieser, G., Lange, S.: Incremental learning of approximations from positive data. Information Processing Letters 89, 37–42 (2004)
Jantke, K.-P.: Monotonic and non-monotonic inductive inference of functions and patterns. In: Dix, J., Schmitt, P.H., Jantke, K.P. (eds.) NIL 1990. LNCS, vol. 543, pp. 161–177. Springer, Heidelberg (1991)
Jain, S., Moelius, S.E., Zilles, S.: Learning without coding. Theoretical Computer Science 473, 124–148 (2013)
Jain, S., Osherson, D., Royer, J., Sharma, A.: Systems that Learn: An Introduction to Learning Theory, 2nd edn. MIT Press, Cambridge (1999)
Kötzing, T.: Abstraction and Complexity in Computational Learning in the Limit. PhD thesis, University of Delaware (2009), http://pqdtopen.proquest.com/#viewpdf?dispub=3373055
Kötzing, T.: A Solution to Wiehagen’s Thesis. In: Symposium on Theoretical Aspects of Computer Science (STACS 2014), pp. 494–505 (2014)
Lange, S., Grieser, G.: On the power of incremental learning. Theoretical Computer Science 288, 277–307 (2002)
Lange, S., Grieser, G.: Variants of iterative learning. Theoretical Computer Science 292, 359–376 (2003)
Lange, S., Zeugmann, T.: Monotonic versus non-monotonic language learning. In: Brewka, G., Jantke, K.P., Schmitt, P.H. (eds.) NIL 1991. LNCS, vol. 659, pp. 254–269. Springer, Heidelberg (1993)
Lange, S., Zeugmann, T.: Incremental learning from positive data. Journal of Computer and System Sciences 53, 88–103 (1996)
Lange, S., Zeugmann, T., Zilles, S.: Learning indexed families of recursive languages from positive data: a survey. Theoretical Computer Science 397, 194–232 (2008)
Osherson, D., Stob, M., Weinstein, S.: Learning strategies. Information and Control 53, 32–51 (1982)
Osherson, D., Stob, M., Weinstein, S.: Systems that Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists. MIT Press, Cambridge (1986)
Osherson, D., Weinstein, S.: Criteria of language learning. Information and Control 52, 123–138 (1982)
Royer, J., Case, J.: Subrecursive Programming Systems: Complexity and Succinctness. Research monograph in Progress in Theoretical Computer Science. Birkhäuser, Basel (1994)
Rogers, H.: Theory of Recursive Functions and Effective Computability. McGraw Hill, New York (1967); Reprinted by MIT Press, Cambridge (1987)
Wiehagen, R.: A thesis in inductive inference. Nonmonotonic and Inductive Logic. In: Dix, J., Schmitt, P.H., Jantke, K.P. (eds.) NIL 1990. LNCS, vol. 543, pp. 184–207. Springer, Heidelberg (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Jain, S., Kötzing, T., Ma, J., Stephan, F. (2014). On the Role of Update Constraints and Text-Types in Iterative Learning. In: Auer, P., Clark, A., Zeugmann, T., Zilles, S. (eds) Algorithmic Learning Theory. ALT 2014. Lecture Notes in Computer Science(), vol 8776. Springer, Cham. https://doi.org/10.1007/978-3-319-11662-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-11662-4_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11661-7
Online ISBN: 978-3-319-11662-4
eBook Packages: Computer ScienceComputer Science (R0)