Model complexity and algorithm selection in classification

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

Volumn 2534, Issue , 2002, Pages 113-126

  Hilario, Melanie ^a  

^a UNIVERSITY OF GENEVA   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

ALGORITHM SELECTION; DEFAULT SETTING; MODEL COMPLEXITY; RELATIVE PERFORMANCE; SINGLE MODELS;

COMPUTATIONAL COMPLEXITY;

EID: 84949749356     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36182-0_12     Document Type: Conference Paper

References (19)

1. E. Alpaydin. GAL: Networks that grow when they learn and shrink when they forget. Technical Report TR-91-032, International Computer Science Institute, Berkeley, May 1991.
2. C. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.
3. Clementine. http://www.spss.com.
4. W. W. Cohen. Fast effective rule induction. In Proc. 11th International Conference on Machine Learning, pages 115–123, 1995.
5. T. G. Dietterich. Statistical tests for comparaing supervised classification learning algorithms. Technical report, DCS, Oregon State University, 1996.
6. S. E. Fahlman and C. Lebiere. The Cascade-Correlation learning architecture. Technical Report CMU-CS-90-100, Carnegie Mellon University, 1990.
7. J. Gama and P. Brazdil. Linear tree. Intelligent Data Analysis, 3:1–22, 1999.
8. D. D. Jensen and P. R. Cohen. Multiple comparisons in induction algorithms. Machine Learning, 38(309–338), 2000.
9. R. Kohavi, G. John, R. Long, D. Manley, and K. Pfleger. MLC++: A machine learning library in C++. Technical report, CSD, Stanford University, 1994.
10. Yann LeCun, J. S. Denker, and S. A. Solla. Optimal brain damage. In Advances in Neural Information Processing, 2, pages 598–605, 1990.
11. T. Lim, W. Loh, and Y. Shih. A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 40:35–75, 2000.
12. MetaL Consortium. Project Homepage. http://www.metal-kdd.org/.
13. D. Michie, D. J. Spiegelhalter, and C. C. Taylor, editors. Machine learning,neur al and statistical classification. Ellis-Horwood, 1994.
14. M. C. Mozer and P. Smolensky. Skeletonization: a technique for trimming the fat from a network via relevance assessment. In D. S. Touretzky, editor, Advances in Neural Information Processing, 1, pages 107–116, San Mateo, CA, 1989. Morgan Kaufmann.
15. G. Nakhaeizadeh and A. Schnabl. Development of multi-criteria metrics for evaluation of data mining algorithms. In Proc. Third Intl. Conf. on Knowledge Discovery and Data Mining, pages 37–42, 1997.
16. B. D. Ripley. Statistical ideas for selecting network architectures. In B. Kappen and S. Gielen, editors, Neural Networks: Artificial Intelligence and Industrial Applications, pages 183–190, London, 1995. Springer.
17. http://www.rulequest.com.
18. T. L. Saaty. Fundamentals of Decision Making and Priority Theory. RWS Publications, 1994.
19. V. Vapnik. The Nature of Statistical Learning Theory. Springer, 1995.