Learning Comprehensible Theories from Structured Data
This tutorial discusses some knowledge representation issues in machine learning. The focus is on machine learning applications for which the individuals that are the subject of learning have complex structure. To represent such individuals,a rich knowledge representation language based on higher-order logic is introduced. The logic is also employed to construct comprehensible hypotheses that one might want to learn about the individuals. The tutorial introduces the main ideas of this approach to knowledge representation in a mostly informal way and gives a number of illustrations. The application of the ideas to decision-tree learning is also illustrated with an example.
KeywordsSugar Convolution Carcinogenicity
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- 1.A.F. Bowers, C. Giraud-Carrier,and J.W. Lloyd.Classification of individuals with complex structure. In P. Langley, editor,Machine Learning:Proceedings of the Seventeenth International Conference (ICML2000), pages 81–88. Morgan Kaufmann,2000.Google Scholar
- 2.A. F. Bowers, C. Giraud-Carrier,and J.W. Lloyd. A knowledge representation framework for inductive learning.http://www.csl.anu.edu.au/~jwl, 2001.
- 6.T. Gäartner, J.W. Lloyd, and P. Flach. Kernels for structured data.In Proceeedings of the 12th International Conference on Inductive Logic Programming (ILP2002). Springer-Verlag, 2002. Lecture Notes in Computer Science.Google Scholar
- 7.D. Haussler.Convolution kernels on discrete structures. Technical Report UCSC-CRL-99-10, University of California in Santa Cruz, Department of Computer Science,1999.Google Scholar
- 9.J.W. Lloyd. Programming in an integrated functional and logic language. Journal of Functional and Logic Programming, 1999(3), March 1999.Google Scholar
- 10.J.W. Lloyd. Knowledge representation,computation, and learning in higher-order logic. http://www.csl.anu.edu.au/~jwl, 2001.
- 11.J.W. Lloyd. Higher-order computational logic. In A. Kakas and F. Sadri, editors, Computational Logic:Logic Programming and Beyond, pages 105–137. Springer-Verlag, LNAI 2407, 2002. Essays in Honour of Robert A.Kowalski, Part I.Google Scholar
- 12.J.W. Lloyd. Predicate construction in higher-order logic. Electronic Transactions on Artificial Intelligence, 4(2000): 21–51, Section B. http://www.ep.liu.se/ej/etai/2000/009/.
- 13.T.M. Mitchell.Machine Learning. McGraw-Hill, 1997.Google Scholar
- 16.S.H. Nienhuys-Cheng and R. deWolf. Foundations of Inductive Logic Programming. Lecture Notes in Arti.cial Intelligence,1228. Springer-Verlag, 1997.Google Scholar
- 17.B. Schölkopf and A. Smola. Learning with Kernels.MIT Press, 2002.Google Scholar
- 18.Home page of Machine Learning Group,The University of York. http://www.cs.york.ac.uk/mlg/.