Abstract
While the popularity of statistical, probabilistic and exhaustive machine learning techniques still increases, relational and logic approaches are still a niche market in research. While the former approaches focus on predictive accuracy, the latter ones prove to be indispensable in knowledge discovery.
In this paper we present a relational description of machine learning problems. We demonstrate how common ensemble learning methods as used in classifier learning can be reformulated in a relational setting. It is shown that multimodal logics and relational data analysis with rough sets are closely related. Finally, we give an interpretation of logic programs as approximations of hypotheses.
It is demonstrated that at a certain level of abstraction all these methods unify into one and the same formalisation which nicely connects to multimodal operators.
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
Orlowska, E.: Reasoning with incomplete information: Rough set based information logics. In: Proceedings of the SOFTEKS Workshop on Incompleteness and Uncertainty in Information Systems, pp. 16–33 (1993)
Yao, Y.Y.: On generalizing rough set theory. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, p. 579. Springer, Heidelberg (2003)
Düntsch, I.: A logic for rough sets. Theoretical Computer Science 179, 427–436 (1997)
Xu, F., Yao, Y., Miao, D.: Rough set approximations in formal concept analysis and knowledge spaces. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds.) Foundations of Intelligent Systems. LNCS (LNAI), vol. 4994, pp. 319–328. Springer, Heidelberg (2008)
Düntsch, I., Gediga, G., Orłowska, E.: Relational attribute systems II: Reasoning with relations in information structures. In: Peters, J.F., Skowron, A., Marek, V.W., Orłowska, E., Słowiński, R., Ziarko, W.P. (eds.) Transactions on Rough Sets VII. LNCS, vol. 4400, pp. 16–35. Springer, Heidelberg (2007)
Wolpert, M.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1, 67–82 (1997)
Breiman, L.: Bagging predictors. Technical Report 421, University of California, Berkeley (1994)
Breiman, L.: Heuristics of instability and stabilization in model selection. The Annals of Statistics 24 (1996)
Schapire, R.E.: The strength of weak learnability. Machine Learning 5, 197–227 (1990)
Schapire, R.E.: The boosting approach to machine learning: An overview. In: MSRI Workshop on Nonlinear Estimation and Classification (2002)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proc. 19th Intl. Conf. Machine Learning (1996)
Pawlak, Z.: On rough sets. Bulletin of the EATCS 24, 94–184 (1984)
Han, X., Lin, T.Y., Han, J.: A new rough sets model based on database systems. Fundamenta Informaticae 59, 135–152 (2003)
Wróblewski, J.: Finding minimal reducts using genetic algorithms. In: Proc. of the Second Annual Joint Conference on Information Sciences (1995)
Øhrn, A.: Discernibility and Rough Sets in Medicine: Tools and Applications. PhD thesis, Norwegian University of Science and Technology, Department of Computer and Information Science (1999)
Lavrač, N., Džeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis-Horwood (1994)
Raedt, L.D.: Logical and Relational Learning. In: Cognitive Technologies. Springer, Heidelberg (2008)
Robinson, J.: A machine-oriented logic based on the resolution principle. J. ACM 12, 23–41 (1965)
Warren, D.H.D.: An abstract prolog instruction set. Technical Note 309, SRI International, Menlo Park, CA (1983)
Muggleton, S., Buntine, W.: Machine invention of first-order predicates by inverting resolution. In: Proceedings of the 5th International Conference on Machine Learning, pp. 339–352. Kaufmann, San Francisco (1988)
Plotkin, G.: A further note on inductive generalization. In: Machine Intelligence, vol. 6. Edinburgh University Press, Edinburgh (1971)
Plotkin, G.: A note on inductive generalisation. In: Meltzer, B., Michie, D. (eds.) Machine Intelligence, vol. 5, pp. 153–163. Edinburgh University Press, Edinburgh (1969)
Shapiro, E.: Inductive inference of theories from facts. In: Lassez, J.L., Plotkin, G. (eds.) Computational logic: essays in honor of Alan Robinson. The MIT Press, Cambridge (1991)
Džeroski, S., Muggleton, S., Russell, S.: PAC-learnability of determinate logic programs. In: Proceedings of the 5th ACM Workshop on Computational Learning Theory, pp. 128–135. ACM Press, New York (1992)
Kautz, H., Kearns, M., Selman, B.: Horn approximations of empirical data. Artificial Intelligence 74 (1995)
Nock, R., Jappy, P.: Function-free Horn clauses are hard to approximate. In: Page, D. (ed.) ILP 1998. LNCS (LNAI), vol. 1446. Springer, Heidelberg (1998)
Quinlan, J.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Quinlan, J., Cameron, R.: Induction of logic programs: FOIL and related systems. New Generation Computing 13, 287–312 (1995)
Muggleton, S.H.: Inverse entailment and Progol. New Generation Computing 13, 245–286 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Müller, M.E. (2009). Modalities, Relations, and Learning. In: Berghammer, R., Jaoua, A.M., Möller, B. (eds) Relations and Kleene Algebra in Computer Science. RelMiCS 2009. Lecture Notes in Computer Science, vol 5827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04639-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-04639-1_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04638-4
Online ISBN: 978-3-642-04639-1
eBook Packages: Computer ScienceComputer Science (R0)