Abstract
In iterative learning the memory of the learner can only be updated when the hypothesis changes; this results in only finitely many updates of memory during the overall learning history. Priced learning relaxes this constraint on the update of memory by imposing some price on the updates of the memory – depending on the current datum – and requiring that the overall sum of the costs incurred has to be finite. There are priced-learnable classes which are not iteratively learnable. The current work introduces the basic definitions and results for priced learning. This work also introduces various variants of priced learning.
Research for this work is supported in part by NUS grants C252-000-087-001 (S. Jain) and R146-000-181-112 (S. Jain and F. Stephan).
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References
Blum, L., Blum, M.: Toward a mathematical theory of inductive inference. Information and Control 28, 125–155 (1975)
Blum, M.: A machine independent theory of the complexity of recursive functions. Journal of the Association of Computing Machinery 14, 322–336 (1967)
Case, J., Jain, S., Lange, S., Zeugmann, T.: Incremental concept learning for bounded data mining. Information and Computation 152, 74–110 (1999)
Freivalds, R., Kinber, E., Smith, C.H.: On the impact of forgetting on learning machines. Journal of the ACM 42, 1146–1168 (1995)
Fulk, M.: Prudence and other conditions on formal language learning. Information and Computation 85, 1–11 (1990)
Mark, E.: Gold. Language identification in the limit. Information and Control 10, 447–474 (1967)
Kinber, E., Stephan, F.: Language learning from texts: mind changes, limited memory and monotonicity. Information and Computation 123, 224–241 (1995)
Lange, S., Zeugmann, T.: Incremental learning from positive data. Journal of Computer and System Sciences 53, 88–103 (1996)
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. Bradford - The MIT Press, Cambridge (1986)
Rogers, H.: Theory of Recursive Functions and Effective Computability. McGraw-Hill, 1967. Reprinted by MIT Press in 1987
Schäfer-Richter, G.: Uber Eingabeabhängigkeit und Komplexität von Inferenzstrategien. Ph.D. Thesis, RWTH Aachen (1984)
Wexler, K., Culicover, P.W.: Formal Principles of Language Acquisition. The MIT Press, Cambridge (1980)
Wiehagen, R.: Limes-Erkennung rekursiver Funktionen durch spezielle Strategien. Elektronische Informationsverarbeitung und Kybernetik (EIK) 12, 93–99 (1976)
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Jain, S., Ma, J., Stephan, F. (2015). Priced Learning. In: Chaudhuri, K., GENTILE, C., Zilles, S. (eds) Algorithmic Learning Theory. ALT 2015. Lecture Notes in Computer Science(), vol 9355. Springer, Cham. https://doi.org/10.1007/978-3-319-24486-0_3
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DOI: https://doi.org/10.1007/978-3-319-24486-0_3
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