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Learning Comprehensible Theories from Structured Data

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Advanced Lectures on Machine Learning

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2600))

Abstract

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.

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References

  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.

  3. A. Church. A formulation of the simple theory of types. Journal of Symbolic Logic, 5:56–68, 1940.

    Article  MATH  MathSciNet  Google Scholar 

  4. T.G. Dietterich, R.H. Lathrop, and T. Lozano-Pℰez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 89: 31–71, 1997.

    Article  MATH  Google Scholar 

  5. P. Finn, S. Muggleton, D. Page, and A. Srinivasan. Pharmacophore discovery using the inductive logic programming system PROGOL. Machine Learning, 30:241–270, 1998.

    Article  Google Scholar 

  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 

  8. J.W. Lloyd. Programming in an integrated functional and logic language. Journal of Functional and Logic Programming, 1999(3), March 1999.

    Google Scholar 

  9. J.W. Lloyd. Knowledge representation,computation, and learning in higher-order logic. http://www.csl.anu.edu.au/~jwl, 2001.

  10. 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 

  11. 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/.

  12. T.M. Mitchell.Machine Learning. McGraw-Hill, 1997.

    Google Scholar 

  13. S. Muggleton. Inductive logic programming. New Generation Computing, 8(4):295–318, 1991.

    Article  MATH  Google Scholar 

  14. S. Muggleton and L. De Raedt. Inductive logic programming:Theory and methods. Journal of Logic Programming, 19/20: 629–679, 1994.

    Article  MathSciNet  Google Scholar 

  15. S.H. Nienhuys-Cheng and R. deWolf. Foundations of Inductive Logic Programming. Lecture Notes in Arti.cial Intelligence,1228. Springer-Verlag, 1997.

    Google Scholar 

  16. B. Schölkopf and A. Smola. Learning with Kernels.MIT Press, 2002.

    Google Scholar 

  17. Home page of Machine Learning Group,The University of York. http://www.cs.york.ac.uk/mlg/.

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Author information

Authors and Affiliations

  1. Research School of Information Sciences and Engineering, The Australian National University, 0200, Canberra, ACT, Australia

    J.W. Lloyd

Authors
  1. J.W. Lloyd
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Editor information

Editors and Affiliations

  1. RSISE, The Australian National University, 0200, Canberra, ACT, Australia

    Shahar Mendelson

  2. Research School for Information Sciences and Engineering, The Australian National University, 0200, Canberra, ACT, Australia

    Alexander J. Smola

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© 2003 Springer-Verlag Berlin Heidelberg

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Lloyd, J. (2003). Learning Comprehensible Theories from Structured Data. In: Mendelson, S., Smola, A.J. (eds) Advanced Lectures on Machine Learning. Lecture Notes in Computer Science(), vol 2600. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36434-X_6

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  • DOI: https://doi.org/10.1007/3-540-36434-X_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00529-2

  • Online ISBN: 978-3-540-36434-4

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