Representational Issues in Meta-Learning

Proceedings, Twentieth International Conference on Machine Learning

Volumn 1, Issue , 2003, Pages 313-320

  Kalousis, Alexandros ^a   Hilario, Melanie ^a  

^a UNIVERSITY OF GENEVA   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA ACQUISITION; KNOWLEDGE ENGINEERING; LEARNING ALGORITHMS; PATTERN RECOGNITION; STATISTICAL METHODS;

DATASET; META-LEARNING;

LEARNING SYSTEMS;

EID: 1942420863     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7, 39-59.
2. Aha, D., Breslow, L., & Munoz-Avila, H. (2001). Conversational case-based reasoning. Applied Intelligence, 14, 9-32.
3. Althoff, K.-D., Muller, W., Nick, M., & Snoek, B. (2000). KM-PEB: An online experience base on knowledge management technology. Advances in Case-Based Reasoning, 5th European Workshop (pp. 335-347). Springer.
4. Armengol, E., & Plaza, E. (2001). Lazy induction of descriptions for relational case-based learning. Proceedings of the 12th European Conference on Machine Learning (pp. 13-24). Springer.
5. Bergmann, R., & Stahl, A. (1998). Similarity measures for object-oriented case representations. Advances in Case-Based Reasoning, 4th European Workshop (pp. 25-36). Springer.
6. Brazdil, P., Carlos, S., & Costa, J. (2003). Ranking learning algorithms. Machine Learning.
7. Cohen, W. (1995). Fast effective rule induction. Proceedings of the 12th International Conference on Machine Learning (pp. 115-123). Morgan Kaufman.
8. Duda, R., Hart, P., & Stork, D. (2001). Pattern classification and scene analysis, chapter Unsupervised Learning and Clustering. John Willey and Sons.
9. Gama, J., & Brazdil, P. (1999). Linear tree. Intelligent Data Analysis, 3, 1-22.
10. Hilario, M., & Kalousis, A. (2001). Fusion of meta-knowledge and meta-data for case-based model selection. Proceedings of the 5th European Conference on Principles and Practice of Knowledge Discovery in Databases. Springer.
11. Kalousis, A., & Theoharis, T. (1999). Noemon: Design, implementation and performance results of an intelligent assistant for classifier selection. Intelligent Data Analysis, 3, 319-337.
12. Kirsten, M., Wrobel, S., & Horvath, T. (2001). Relational data mining, chapter Distance Based Approaches to Relational Learning and Clustering, 212-232. Springer.
13. Lindner, C., & Studer, R. (1999). AST: Support for algorithm selection with a CBR approach. Proceedings of the 16th International Conference on Machine Learning, Workshop on Recent Advances in Meta-Learning and Future Work.
14. Michie, D., Spiegelhalter, D., & Taylor, C. (1994). Machine learning, neural and statistical classification Ellis Horwood Series in Artificial Intelligence.
15. Peng, Y., Flach, P., Soares, C., & Brazdil, P. (2002). Improved dataset characterisation for meta-learning. Proceedings of the 5th International Conference on Discover Science 2002. Springer-Verlag.
16. Pfahringer, B., Bensusan, H., & Giraud-Carrier, C. (2000). Tell me who can learn you and I can tell you who you are: Landmarking various learning algorithms. Proceedings of the 17th International Conference on Machine Learning (pp. 743-750). Morgan Kaufman.
17. Rendell, L., Seshu, R., & Tcheng, D. (1987). Layered concept learning and dynamically variable bias management. Proceedings of the 10th International Joint Conference on AI (pp. 366-372). Morgan Kaufman.
18. Todorovski, L., & Dzeroski, S. (1999). Experiments in meta-level learning with ILP. Proceedings of the 3rd European Conference on Principles of Data Mining and Knowledge Discovery (pp. 98-106). Springer.