Abstract
SURFER is an empirical discovery system that given a set of input data and a modelling vocabulary returns the model that best describes that data. The best model is considered to be the one that minimizes the description length of that model plus the data encoded using that model. The search for models is controlled by the a priori estimate of model likelihoods as encoded in the modelling vocabulary. SURFER includes domain independent mechanisms for identifying redundant models and for finding free parameters. The system is described together with the results of running the system on several different types of problems.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
M. M. Kokar. Determining arguments of invariant functional descriptions. Machine Learning, 1:403–422, 1986.
John R. Koza. Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems. Technical Report STAN-CS-90-1314, Stanford University, 1990.
Pat Langley and Jan M. Zytkow. Data-driven approaches to empirical discovery. Artificial Intelligence, 40(1):283–312, 1989.
John R. Pierce. An Introduction to Information Theory. Dover Publications, Inc, New York, NY, 1980.
William H. Press, Brian P. Flannery, Saul A. Teukolsky, and William T. Vetterling. Numerical Recipes in C. Cambridge University Press, Cambridge, England, 1988.
J. Rissanen. Modeling by shortest data description. Automatica, 14:465–471, 1978.
Hanan Samet. The Design and Analysis of Spatial Data Structures. Addison-Wesley, New York, 1990.
R. J. Solomonoff. A formal theory of inductive inference. parts i and ii. Information and Control, 7:1–22, 224–254, 1964.
R. J. Solomonoff. A system for incremental learning based on algorithmic probability. In AAAI Symposium on the Theory and Application of Minimal-Length Encoding, pages 140–146, March 1990.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1995 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
McConnell, C. (1995). Minimal model complexity search. In: Pinto-Ferreira, C., Mamede, N.J. (eds) Progress in Artificial Intelligence. EPIA 1995. Lecture Notes in Computer Science, vol 990. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60428-6_15
Download citation
DOI: https://doi.org/10.1007/3-540-60428-6_15
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60428-0
Online ISBN: 978-3-540-45595-0
eBook Packages: Springer Book Archive