Abstract
The version space approach suffers from two main problems, i. e. inability of inducing concepts consistent with data due to use of a restricted hypothesis space and lack of computational efficiency. In this paper we investigate the use of multiple concept languages in a version space approach. We define a graph of languages ordered by the size of their associated concept sets, and provide a procedure for efficiently inducing version spaces while shifting from small to larger concept languages. We show how this framework can help overcome the two above-mentioned limitations. Also, compared to other work on language shift, our approach suggests an alternative strategy for searching the space of new concepts, which is not based on constructive operators.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Bundy, A., Silver, B., Plummer, D. (1985). An analytical comparison of some rule-learning problems. Artificial Intelligence, 27.
Carpineto, C. (1991). Analytical negative generalization and empirical negative generalization are not cumulative: a case study. In Proceedings of EWSL-91, Lecture Notes on Artificial Intelligence, Springer-Verlag.
Dietterich, T.G., London B., Clarkson K., Dromey R.G. (1982). Learning and inductive inference. In Cohen & Feigenbaum (Eds.) The Handbook of Artificial Intelligence, Morgan Kaufmann.
Haussler, D. (1988). Quantifying inductive bias: Artificial Intelligence learning algorithms and Valiant's learning framework. Artificial Intelligence, 36.
Hirsh, H. (1989). Combining Empirical and Analytical Learning with Version Spaces. In Proceedings of the sixth international workshop on Machine Learning. Syracuse, Morgan Kaufmann.
Matheus, C.J., Rendell, L.A. (1989). Constructive induction on decision trees. In Proceedings of the 11th IJCAI, Detroit, Morgan Kaufmann.
Michalski, R.S. (1983). A theory and methodology of inductive learning. Artificial Intelligence, 20, 111–161
Mitchell, T.M. (1982). Generalization as Search. Artificial Intelligence, 18, 203–226.
Pagallo, G.(1989). Learning DNF by Decision Trees. In Proceedings of the 11th IJCAI, Detroit, Morgan Kaufmann.
Smith, Rosenbloom, P., (1990). Incremental Non-Backtracking Focusing: A Poliniomally Bounded Generalization Algorithm for Version Spaces. In Proceedings of the eighth AAAI, Boston, Morgan Kaufmann.
Subramanian, D., Feigenbaum, J. (1986). Factorization in Experiment Generation. In Proceedings of the fifth AAAI, Philadelphia, Morgan Kaufmann.
Utgoff, P.E. (1986). Shift of bias for inductive concept learning. In R. Michalski et al. (Eds), Machine Learning II. Morgan Kaufmann, Palo Alto, California.
Wogulis, J., Langley, P. (1989). Improving efficiency by learning intermediate concepts. In Proceedings of the 11th IJCAI, Detroit, Morgan Kaufmann.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1991 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Carpineto, C. (1991). Version-space induction with multiple concept languages. In: Ardizzone, E., Gaglio, S., Sorbello, F. (eds) Trends in Artificial Intelligence. AI*IA 1991. Lecture Notes in Computer Science, vol 549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54712-6_232
Download citation
DOI: https://doi.org/10.1007/3-540-54712-6_232
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-54712-9
Online ISBN: 978-3-540-46443-3
eBook Packages: Springer Book Archive