Combining model-based and instance-based learning for first order regression

ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Volumn , Issue , 2005, Pages 193-200

  Driessens, Kurt ^a   Džeroski, Sašo ^b  

^a UNIVERSITY OF WAIKATO   (New Zealand)

^b JO EF STEFAN INSTITUTE   (Slovenia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTATION THEORY; MATHEMATICAL MODELS; REGRESSION ANALYSIS;

FUNCTION APPROXIMATION; INSTANCE-BASED LEARNING; MODEL-BASED LEARNING; REGRESSION ALGORITHM;

LEARNING SYSTEMS;

EID: 31844440221     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1102351.1102376     Document Type: Conference Paper

References (16)

1. Aha, D., Kibler, D., & Albert, M. (1991). Instance-based learning algorithms. Machine Learning, 6, 37-66.
2. Atkeson, C. G., Moore, A. W., & Schaal, S. (1997). Locally weighted learning, Artificial Intelligence Review, 11, 11-73.
3. Blockeel, H., & De Raedt, L. (1998). Top-down induction of first order logical decision trees. Artificial Intelligence, 101, 285-297.
4. Domingos, P. (1996). Unifying instance-based and rule-based induction. Machine Learning, 24, 141-168.
5. Driessens, K., & Džeroski, S. (2004). Integrating guidance into relational reinforcement learning. Machine Learning, 57, 271-304.
6. Driessens, K., & Ramon, J. (2003). Relational instance based regression for relational reinforcement learning. Proceedings of the Twentieth International Conference on Machine Learning (pp. 123-130). AAAI Press.
7. Driessens, K., Ramon, J., & Blocked, H. (2001). Speeding up relational reinforcement learning through the use of an incremental first order decision tree learner. Proceedings of the 13th European Conference on Machine Learning (pp. 97-108). Springer-Verlag.
8. Džeroski, S., De Raedt, L., & Driessens, K. (2001). Relational reinforcement learning. Machine Learning, 43, 7-52.
9. Džeroski, S., De Raedt, L., & Blockeel, H. (1998). Relational reinforcement learning. Proceedings of the 15th International Conference on Machine Learning (pp. 136-143). Morgan Kaufmann.
10. Freidman, J., Bentley, J., & Finkel, R. (1977). An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw., 3, 209-226.
11. Gärtner, T., Driessens, K., & Ramon, J. (2003). Graph kernels and Gaussian processes for relational reinforcement learning. Inductive Logic Programming, 13th International Conference, IIP 2003, Proceedings (pp. 146-163). Springer.
12. Moore, A. (1991). An introductory tutorial on kd-trees (Technical Report). Robotics Institute, Carnegie Mellon University.
13. Quinlan, J. (1993). Combining instance-based and model-based learning. Proceedings of the 10th International Conference on Machine Learning. Morgan Kaufmann.
14. Ramon, J., & Bruynooghe, M. (2001). A polynomial time computable metric between point sets. Acta Informatica, 37, 765-780.
15. Salzberg, S. (1991). A nearest hyperrectangle learning method. Machine Learning, 6, 252-276.
16. Wagner, R., & Fischer, M. (1974). The string to string correction problem. Journal of the ACM, 21, 168-173.