Maximum likelihood topology preserving ensembles

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

Volumn 4224 LNCS, Issue , 2006, Pages 1434-1442

  Corchado, Emilio ^a   Baruque, Bruno ^a   Gabrys, Bogdan ^b  

^a UNIVERSITY OF BURGOS   (Spain)

^b BOURNEMOUTH UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA STRUCTURES; LEARNING SYSTEMS; SELF ORGANIZING MAPS; STATISTICAL METHODS; TOPOLOGY;

MAXIMUM LIKELIHOOD TOPOLOGY; PREDICTION SYSTEM; RESAMPLING TECHNIQUES; STABILIZING EFFECT;

MAXIMUM LIKELIHOOD ESTIMATION;

EID: 33750569111     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11875581_170     Document Type: Conference Paper

References (19)

1. Kohonen, T. Self-Organization and Associative Memory. Springer-Verlag, Heidelberg, Germany, 1984.
2. Kohonen, T. Barna, G and Chrisley R. Statistical Pattern Recognition with Neural Networks. In Proceeding of International Joint Conference of Neural Networks (pp. 61-88), IEEE Press, 1988.
3. Kohonen, T. The Self-Organizing Map. In Proceedings of the IEEE 78 (pp. 1464-1480), 1990.
4. Corchado, E. and Fyfe, C. Maximum Likelihood Topology Preserving Algorithms. In Proceedings of the U.K. Workshop on Computational Intelligence, Birmingham, UK, 2002.
5. Corchado, E. and Fyfe C. The Scale Invariant Map and Maximum Likelihood Hebbian Learning. International Conference on Knowledge-Based & Intelligent Information & Engineering System, IOS Press, 2002.
6. Corchado, E., MacDonald, D., Fyfe C., Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit. Data Mining Knowledge Discovery 8(3): 203-225 (2004).
7. Friedman J., Tukey. J.: A Projection Pursuit Algorithm for Exploratory Data Analysis. IEEE Transaction on Computers, Vol. 23 (1974) 881-890.
8. Hyvärinen A.: Complexity Pursuit: Separating Interesting Components from Time Series. Neural Computation, Vol. 13(4) (2001) 883-898.
9. Fyfe, C. and Corchado, E. Maximum likelihood Hebbian rules. ESANN (European Symposium on Artificial Neural Networks), ISBN 2-930307-02-1, 2002.
10. Breiman, L. Bagging Predictors. Machine Learning, 24 (pp. 123-140), 1996.
11. Ruta, D. and Gabrys, B. A Theoretical Analysis of the Limits of Majority Voting Errors for Multiple Classifier Systems, Pattern Analysis and Applications, vol. 5, pp. 333-350, 2002.
12. Schapire, R.E; Freud, Y; Bartlett, P. and Lee, W.S. Boosting the margin: a new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5): 1651-1686, 1998.
13. Gabrys, B. Learning Hybrid Neuro-Fuzzy Classifier Models From Data: To Combine or not to Combine? Fuzzy Sets and Systems, vol. 147, pp. 39-56, 2004.
14. Kuncheva, L. Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, 2004.
15. Ruta, D. and Gabrys, B. Classifier Selection for Majority Voting, Special issue of the journal of information fusion on Diversity in Multiple Classifier Systems, vol. 6, issue 1, pp. 63-81, 1 March 2005.
16. Fyfe, C. A Scale Invariant Map. Network: Computation in Neural Systems, 7 (pp 269-275), 1996.
17. Petrakieva, L. and Fyfe, C. Bagging and Bumping Self-organising Maps. Computing and Information Systems, 2003.
18. Gabrys, B., Baruque, B. and Corchado, E. Outlier Resistant PCA Ensembles. To appear in the proceedings of the International Conference on Knowledge-Based & Intelligent Information & Engineering System, KES'2006, 2006.
19. Gabrys, B., Combining Neuro-Fuzzy Classifiers for Improved Generalisation and Reliability, Proceedings of the Int. Joint Conference on Neural Networks, (IJCNN'2002) a part of the WCCI'2002 Congress, ISBN: 0-7803-7278-6, pp. 2410-2415, Honolulu, USA, May 2002.