Evaluation of the information-theoretic construction of multiple classifier systems

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

Volumn 2003-January, Issue , 2003, Pages 789-793

  Kang, Hee Joong ^a   Doermann, David ^b  

^a Hansung University   (South Korea)

^b UNIVERSITY OF MARYLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHARACTER RECOGNITION; INFORMATION THEORY;

COMPONENT CLASSIFIERS; CONCORDIA UNIVERSITY; HANDWRITTEN NUMERAL; INFORMATION THEORETIC CRITERION; INFORMATION-THEORETIC METHODS; MULTIPLE CLASSIFIER SYSTEMS; NUMBER OF COMPONENTS; UNIVERSITY OF CALIFORNIA;

CLASSIFICATION (OF INFORMATION);

EID: 84858337789     PISSN: 15205363     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDAR.2003.1227770     Document Type: Conference Paper

References (13)

1. C. Blake and C. Merz. UCI repository of machine learning databases [http://www.ics.uci.edu/∼mlearn/mlrepository.html]. Irvine, CA, Dept. of Infor. and Comp. Sciences, 1998.
2. S. Impedovo and A. Salzo. Evaluation of Combination Methods. In Proc. of the 5th ICDAR, pages 394-397, 1999.
3. H.-J. Kang and S.-W. Lee. A Dependency-based Framework of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals. In Proc. of 1999 IEEE Comp. Soc. Conf. on CVPR, volume 2, pages 124-129, 1999.
4. H.-J. Kang and S.-W. Lee. Combining Classifiers based on Minimization of a Bayes Error Rate. In Proc. of the 5th ICDAR, pages 398-401, 1999.
5. H.-J. Kang and S.-W. Lee. An Information-Theoretic Strategy for Constructing Multiple Classifier Systems. In Proc. of the 15th ICPR, volume 2, pages 483-486, 2000.
6. J. Kittler, M. Hatef, R. P. W. Duin, and J. Matas. On Combining Classifiers. IEEE TPAMI, 20(3):226-239, 1998.
7. P. M. Lewis. Approximating Probability Distributions to Reduce Storage Requirement. Information and Control, 2:214- 225, Sep. 1959.
8. T. Matsui, T. Tsutsumida, and S. N. Srihari. Combination of Stroke/Background Structure and Contour-direction Features in Handprinted Alphanumeric Recognition. In Proc. of the 4th IWFHR, pages 87-96, 1994.
9. I.-S. Oh, J.-S. Lee, K.-C. Hong, and S.-M. Choi. Class-expert approach to unconstrained handwritten numeral recognition. In Proc. of the 5th IWFHR, pages 35-40, 1996.
10. I.-S. Oh and C. Y. Suen. Distance features for neural network-based recognition of handwritten characters. IJDAR, 1(2):73-88, 1998.
11. C. Y. Suen, C. Nadal, R. Legault, T. A. Mai, and L. Lam. Computer Recognition of Unconstrained Handwritten Numerals. Proc. of IEEE, pages 1162-1180, 1992.
12. D. C. C. Wang and A. K. C. Wong. Classification of Discrete Data with Feature Space Transform. IEEE TAC, AC- 24(3):434-437, 1979.
13. K. Woods, W. P. Kegelmeyer Jr., and K. Bowyer. Combinition of Multiple Classifiers Using Local Accuracy Estimates. IEEE TPAMI, 19(4):405-410, 1997.