AST: Support for algorithm selection with a CBR approach

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1704, Issue , 1999, Pages 418-423

  Lindner, Guido ^a   Studer, Rudi ^b  

^a DAIMLER AG   (Germany)

^b UNIVERSITY OF KARLSRUHE   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CASE BASED REASONING;

ALGORITHM SELECTION; BOTTOM-UP STRATEGY; CASE RETRIEVAL; CASE-BASED REASONING APPROACHES; DATA MINING ALGORITHM; KNOWLEDGE DISCOVERY IN DATABASE; KNOWLEDGE DISCOVERY PROCESS; SYSTEMATIC ANALYSIS;

DATA MINING;

EID: 84956869980     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-48247-5_52     Document Type: Conference Paper

References (9)

1. R. Engels, G. Lindner, and R. Studer. A guided tour through the data mining jungle. In D. Heckerman, H. Manilla and D. Pregibon, editor, Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, Newport Beach, CA, August 14 -17, 1997. AAAI Press, Menlo Park, CA.
2. R. Engels and Ch. Tlieusinger. Using a data metric for offering preprocessing advice in data mining applications. In Henri Prade, editor, Proceedings of the 13th biennial European Conference on Artificial Intelligence (ECAI-98), pages 430-434. John Wileys & Sons, 1998.
3. Joao Gama and Pavel Brazdil. Characterization of classification algorithms. In N. Mamede and C. Ferreira, editors. Advances on Artificial Intelligence - EPIA95. Springer Verlag, 1995.
4. Y. Krodratoff, D. Sleeman, M. Uszynski, K. Causse, and S. Craw. Enhancing the Knowledge Engineering Process, chapter Building a Machine Learning Toolbox. Elsevier Science Publishers, North Holland, 1992.
5. G. Lindner and R. Studer. AST: Support for Algorithm Selection with a CBR Approach. In Recent Advances in Meta Learning and Future Work, Workshop Proceedings of the ICML 1999, Bled, Slowenien, June 1999.
6. C. J. Merz and P. M. Murphy. Uci repository of machine learning databases. [http://www.ics.uci.edu/~mlearn/MLRepository.html], 1996. Irvine, CA: University of California, Department of Information and Computer Science.
7. D. Micliie, C. Taylor, and D. Spiegelhalter. Machine Learning, Neural and Statistical Classification. Ellis Hoorwood, 1994.
8. Cullen Schaffer. A conservation law for generalization performance. In Proc. Eleventh Intern. Conf. on Machine Learning, pages 259-265, Palo Alto, CA, 1994. Morgan Kaufmann.
9. D. Sleeman, M. Rissakis, S. Craw, and N. Graner. Consult 2 Pre- and Post of Machine Learning Applications. International Journal of Human Computer Studies, (43):43-63, 1995