Abstract
As some cognitive research suggests, in the process of learning languages, in addition to overt explicit negative evidence, a child often receives covert explicit evidence in form of corrected or rephrased sentences. In this paper, we suggest one approach to formalization of overt and covert evidence within the framework of one-shot learners via subset and membership queries to a teacher (oracle). We compare and explore general capabilities of our models, as well as complexity advantages of learnability models of one type over models of other types, where complexity is measured in terms of number of queries. In particular, we establish that “correcting” positive examples give sometimes more power to a learner than just negative (counter)examples and access to full positive data.
This is a preview of subscription content, log in via an institution to check access.
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)
Angluin, D.: Queries and concept learning. Machine Learning 2, 319–342 (1988)
Becerra-Bonache, L., Bibire, C., Dediu, A.H.: Learning dfa from corrections. In: Fernau, H. (ed.) Theoretical Aspects of Grammar Induction (TAGI), pp. 1–12 (2005). WSI–2005–14
Becerra-Bonache, L., Dediu, A.H., Tîrnăucă, C.: Learning dfa from correction and equivalence queries. In: Sakakibara, Y., Kobayashi, S., Sato, K., Nishino, T., Tomita, E. (eds.) ICGI 2006. LNCS (LNAI), vol. 4201, pp. 281–292. Springer, Heidelberg (2006)
Gold, E.M.: Language identification in the limit. Information and Control 10, 447–474 (1967)
Gasarch, W., Smith, C.: Learning via queries. Journal of the ACM, 649–674 (1991)
Jain, S., Kinber, E.: Learning languages from positive data and negative counterexamples. Journal of Computer and System Sciences (to appear, 2007)
Jain, S., Kinber, E.: One-shot learners using negative counterexamples and nearest positive examples. Technical Report TRA3/07, School of Computing, National University of Singapore (2007)
Jain, S., Lange, S., Zilles, S.: Gold-style and query learning under various constraints on the target class. In: Jain, S., Simon, H.U., Tomita, E. (eds.) ALT 2005. LNCS (LNAI), vol. 3734, pp. 226–240. Springer, Heidelberg (2005)
Lange, S., Zilles, S.: Formal language identification: Query learning vs gold-style learning. Information Processing Letters 91, 285–292 (2004)
Lange, S., Zilles, S.: Relations between gold-style learning and query learning. Information and Computation 203, 211–237 (2005)
Rogers, H.: Theory of Recursive Functions and Effective Computability. McGraw-Hill (1967) (reprinted by MIT Press in 1987)
Rohde, D.L.T., Plaut, D.C.: Language acquisition in the absence of explicit negative evidence: how important is starting small? Cognition 72, 67–109 (1999)
Zeugmann, T., Lange, S.: A guided tour across the boundaries of learning recursive languages. In: Lange, S., Jantke, K.P. (eds.) Algorithmic Learning for Knowledge-Based Systems. LNCS, vol. 961, pp. 190–258. Springer, Heidelberg (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jain, S., Kinber, E. (2007). One-Shot Learners Using Negative Counterexamples and Nearest Positive Examples. In: Hutter, M., Servedio, R.A., Takimoto, E. (eds) Algorithmic Learning Theory. ALT 2007. Lecture Notes in Computer Science(), vol 4754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75225-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-540-75225-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75224-0
Online ISBN: 978-3-540-75225-7
eBook Packages: Computer ScienceComputer Science (R0)