Browse > Article

Combining Multiple Classifiers using Product Approximation based on Third-order Dependency  

강희중 (한성대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.5, 2004 , pp. 577-585 More about this Journal
Abstract
Storing and estimating the high order probability distribution of classifiers and class labels is exponentially complex and unmanageable without an assumption or an approximation, so we rely on an approximation scheme using the dependency. In this paper, as an extended study of the second-order dependency-based approximation, the probability distribution is optimally approximated by the third-order dependency. The proposed third-order dependency-based approximation is applied to the combination of multiple classifiers recognizing handwritten numerals from Concordia University and the University of California, Irvine and its usefulness is demonstrated through the experiments.
Keywords
third-order dependency; combining multiple classifiers; product approximation; measure of closeness; Bayesian combination method; handwritten numeral;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 강희중, 이성환, '무제약 필기 숫자를 인식하기 위한 다수 인식기를 결합하는 의존관계 기반의 프레임워크', 정보과학회논문지 : 소프트웨어 및 응용, 제27건, 제8호, pp.855-863, 2000   과학기술학회마을
2 L. Xu and A. Krzyzak, and C. Y. Suen, 'Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition,' IEEE Trans. on Systems, Man and Cybernetics, vol.22, no.3, pp.418-435, 1992   DOI   ScienceOn
3 Y. S. Huang and C. Y. Suen, 'A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.17, no.1, pp.90-94, 1995   DOI   ScienceOn
4 Mandler, E. and Schuermann, J., 'Combining the classification results of independent classifiers based on the dempster/shafer theory of evidence,' In E. S. Gelsema and L. N. Kanal, editors, Pattern Recognition and Artifical Intelligence, pp. 381-393, 1988
5 D.-S. Lee and S. N. Srihari, 'Handprinted Digit Recognition : A Comparison of Algorithms,' Proc. of the 3rd Int. Workshop on Frontiers in Handwriting Recognition, pp.42-45, 1993
6 C. K. Chow and C. N. Liu, 'Approximating Discrete Probability Distributions with Dependence Trees,' IEEE Trans. on Information Theory, vol.14, no.3, pp.462-467, 1968   DOI
7 P. M. Lewis, 'Approximating Probability Distributions to Reduce Strange Requirement,' Information and Control, vol.2, pp.214-225, Sep., 1959   DOI
8 Matsui, T., Takamura, T., Srihari, S. N., 'Combination of stroke/Background Structure and Contour-direction Features in Handprinted Alphanumeric Recognition,' In Proceedings of the 4th Int. Workshop on Frontiers in Handwriting Recognition, pp. 87-96, 1994
9 Oh, I.-S. and Suen, C. Y., 'Distance features for neural network-based recognition of handwritten characters,' International Journal on Document Analysis and Recognition, 1(2):73-80, 1998   DOI   ScienceOn
10 Oh, I.-S., Lee, I.-S., Hong, K.-S. and Choi, S.-M., 'Class-expert approach use unconstrained handwritten numeral recognition,' Proceedings of the 5th Int. Workshop on Frontiers in Handwriting Recognition, pp.35-40, 1996
11 Suen, C. Y., Nidal, C., Legault, R., Mai, T. A., and Lam, L., 'Computer Recognition of Unconstrained Handwritten Numerals,' In Proceedings of IEEE, pp. 1162-1180, 1992   DOI   ScienceOn
12 Blake, C. and Merz, C., UCI repository of machine learning databases [http://www.ics.uci.edu/~mlearn/mlrepository.html], Irvine, CA, Dept. of Information and Computer Sciences, 1998