Decision templates based RBF network for tree-structured multiple classifier fusion

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

Volumn 5519 LNCS, Issue , 2009, Pages 92-101

  Abdel Hady, Mohamed Farouk ^a   Schwenker, Friedhelm ^a  

^a UNIVERSITY OF ULM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CLASSIFIERS; BINARY PROBLEMS; COMBINATION METHOD; DECISION TEMPLATE; DEMPSTER SHAFER EVIDENCE THEORY; FINAL DECISION; FUSION METHODS; INTERMEDIATE RESULTS; KEY FACTORS; MULTICLASS PATTERN RECOGNITION; MULTIPLE CLASSIFIER FUSION; RBF NETWORK; STATISTICAL INFORMATION; STRUCTURED APPROACH; TREE ENSEMBLES; TREE STRUCTURES;

DECISION TREES; LEARNING SYSTEMS; PATTERN RECOGNITION; RADIAL BASIS FUNCTION NETWORKS;

CLASSIFIERS;

EID: 70349410552     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02326-2_10     Document Type: Conference Paper

References (14)

1. Kumar, S., Ghosh, J., Crawford, M.: A hierarchical multiclassifier system for hyperspectral data analysis. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 270-279. Springer, Heidelberg (2000)
2. Kumar, S., Ghosh, J., Crawford, M.: Hierarchical fusion of multiple classifiers for hyperspectral data analysis. International Journal of Pattern Analysis and Applications 5(2), 210-220 (2002)
3. Fay, R., Schwenker, F., Thiel, C., Palm, G.: Hierarchical neural networks utilising Dempster-Shafer evidence theory. In: Schwenker, F., Marinai, S. (eds.) ANNPR 2006. LNCS (LNAI), vol. 4087, pp. 198-209. Springer, Heidelberg (2006)
4. Fay, R.: Feature selection and information fusion in hierarchical neural networks for iterative 3D-object recognition. Ph.D thesis, Ulm University (2007)
5. Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via errorcorrecting output codes. Journal of Artificial Intelligence Research 2(1), 263-286 (1995)
6. Kuncheva, L., Bezdek, J., Duin, R.: Decision templates for multiple classifier fusion: An experimental comparison. Pattern Recognition 34(2), 299-314 (2001)
7. Dempster, A.P.: A generalization of bayesian inference. Journal of the Royal Statistical Society, 205-247 (1968)
8. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
9. Wolpert, D.H.: Stacked generalization. Neural Networks 5(6), 1289-1301 (1994)
10. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, Hoboken (2004)
11. Schwenker, F., Kestler, H., Palm, G.: Three learning phases for radial basis function networks. Neural Networks 14, 439-458 (2001)
12. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)
13. Blake, C., Merz, C.: UCI repository of machine learning databases. University of California (1998), http://www.ics.uci.edu/̃mlearn/ MLRepository.html
14. Nadeau, C., Bengio, Y.: Inference for the generalization error. Machine Learning 62, 239-281 (2003)