Abstract
In this paper we present a machine learning workbench, which we have developed by making use of deductive object-oriented database (DOOD) technology. It provides a comfortable environment for performing a large variety of machine learning tasks. By deriving full benefit of the available powerful logic and object-oriented programming language, we have implemented an easily extendable representative collection of machine learning algorithms. As realistic case study for the feasibility of the workbench we applied it to the automatic acquisition of linguistic knowledge within a natural language database interface.
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Aha, D. W., Kibler, D., Albert, M.: Instance-based learning algorithms. Machine Learning 7 (1991) 37–66
Barja, M. L. et al.: An effective deductive object-oriented database through language integration. Proc. of the Intl. Conf. on Very Large Data Bases (1994) 463–474
Daelemans, W., van den Bosch, A.: Generalisation performance of backpropagation learning on a syllabification task. Drossaers, M., Nijholt, A. (eds): TWLT3: Connectionism and Natural Language Processing. Twente University Press, Enschede (1992) 27–37
Daelemans, W., van den Bosch, A., Weijters, T.: IGTree: Using trees for compression and classification in lazy learning algorithms. Artificial Intelligence Review (to appear)
Imielinski, T., Mannila, H.: A database perspective on knowledge discovery. Communications of the ACM 39:11 (1996) 58–64
Muggleton, S. (ed): Inductive Logic Programming. Academic Press, London (1992)
Quinlan, J. R.: Induction of decision trees. Machine Learning 1 (1986) 81–206
Quinlan, J. R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, California (1993)
Quinlan, J. R., Cameron-Jones, R. M.: Induction of logic programs: FOIL and related systems. New Generation Computing 13 (1995) 287–312
Winiwarter, W.: The Integrated Deductive Approach to Natural Language Interfaces. Ph.D. thesis, University of Vienna (1994)
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© 1997 Springer-Verlag Berlin Heidelberg
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Winiwarter, W., Kambayashi, Y. (1997). A machine learning workbench in a DOOD framework. In: Hameurlain, A., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 1997. Lecture Notes in Computer Science, vol 1308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0022054
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DOI: https://doi.org/10.1007/BFb0022054
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Publisher Name: Springer, Berlin, Heidelberg
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