Relational IBL in classical music

Machine Learning

Volumn 64, Issue 1-3, 2006, Pages 5-24

  Tobudic, Asmir ^a   Widmer, Gerhard ^b  

^a AUSTRIAN RESEARCH INSTITUTE FOR ARTIFICIAL INTELLIGENCE   (Austria)

^b JOHANNES KEPLER UNIVERSITY LINZ   (Austria)

Author keywords

Music; Relational instance based learning

Indexed keywords

CLASSICAL MUSIC; K-NN ALGORITHM; RELATIONAL INSTANCE-BASED LEARNING;

ALGORITHMS; INFORMATION TECHNOLOGY; LARGE SCALE SYSTEMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBLEM SOLVING;

COMPUTER MUSIC;

EID: 33748280644     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-006-8260-4     Document Type: Conference Paper

References (19)

1. Bisson, G. (1992). Learning in FOL with a similarity measure. In Proceedings of the 10th AAAI, 1992.
2. De Raedt, L. (1992). Interactive theory revision: An inductive logic programming approach. Academic Press.
3. Duda, R., & Hart, P. (1967). Pattern classification and scene analysis. New York, NY: John Wiley & Sons.
4. Dzeroski, S., Schulze-Kremer, H. K. R., Siems, K., Wettschereck, D., & Blockeel, H. (1998). Diterpene structure elucidation from 13C NMR spectra with inductive logic programming. Applied Artificial Intelligence: Special Issue on First-Order Knowledge Discovery in Databases, 12(5), 363-384.
5. Emde, D. & Wettschereck, D. (1996). Relational instance-base learning. In Proceedings of the Thirteen International Conference on Machine Learning (ICML'96) (pp. 122-130). SanMateo: Morgan Kaufmann.
6. Helft, N. (1989). Induction as nonmonotonic inference. In Proceedings of the. 1st international Conference on Principles of Knowledge Representation and Reasoning (pp. 149-156). Kaufmann.
7. Muggleton, S. H. & Feng, C. (1990). Efficient induction of logic programs. In Proceedings of the First Conference on Algorithmic Learning Theory, Tokyo.
8. Muggleton, S., & De Raedt, L. (1994). Inductive logic programming: Theory and methods. Journal of Logic Programming, 19,20, 629-679.
9. Ramon, J., & Bruynooghe, M (1998). A framework for defining distances between first-order logic objects. In D. Page (ed.), Proceedings of the 8th International Conference on Inductive Logic Programming, vol. 1446 of Lecture Notes in Artificial Intelligence (pp. 271-280). Springer-Verlag.
10. Ramon, J. & Bruynooghe, M. (2001). A polynomial time computable metric between point sets. Acta Informatica, 37, 765-780.
11. Rouveirol, C. (1992). Extensions of inversion of resolution applied to theory completion. In S. Muggleton (ed.), Inductive logic programming. London: Academic Press.
12. Sebag, M. & Rouveirol, C. (1997). Tractable induction and classification in FOL via stochastic matching. In Proceedings of IJCAI-97. Morgan Kaufmann.
13. Todd, N. McA. (1992). The dynamics of dynamics: A model of musical expression. Journal of the Acoustical Society of America, 91, 3540-3550.
14. Tobudic, A. & Widmer, G. (2003a). Playing Mozart phrase by phrase. In Proceedings of 5th International Conference on Case-Based Reasoning (ICCBR'03), Trondheim, Norway. Berlin: Springer Verlag.
15. Tobudic, A. & Widmer, G. (2003b). Relational IBL in music with a new structural similarity measure. In Proceeding of 9th International Conference, on Inductive Logic Programming (ILP'03). Szeged, Hungary. Berlin: Springer Verlag.
16. Widmer, G. (2002). Machine discoveries: A few simple, robust local expression principles. Journal of New Music Research, 31(1), 37-50.
17. Widmer, G. (2003). Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries. Artificial Intelligence 146(2), 129-148.
18. Widmer, G., Dixon, S., Goebl, W., Pampalk, E., & Tobudic, A. (2003). In search of the horowitz factor. AI Magazine, 24(3), 111-130.
19. Widmer, G., & Tobudic, A. (2003). Playing Mozart by analogy. Journal of New Music Research, 32(3), 259-268.