Dependency-based framework of combining multiple experts for the recognition of unconstrained handwritten numerals

Hee Joong Kang, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingChapter

9 Citations (Scopus)

Abstract

Although Behavior-Knowledge Space (BKS) method does not need any assumptions in combining multiple experts, it should build theoretically exponential storage spaces for storing and managing jointly observed K decisions from K experts. That is, combining K experts needs a (K+1)st-order probability distribution. However, it is well known that the distribution becomes unmanageable in storing and estimating, even for a small K. In order to overcome such weakness, it would be attractive to decompose the distribution into a number of component distributions and to approximate the distribution with a product of the component distributions. One of such previous works is to apply a conditional independence assumption to the distribution. Another work is to approximate the distribution with a product of only first-order tree dependencies or second-order distributions as shown in [1]. In this paper, a dependency-based framework is proposed to optimally approximate a probability distribution with a product set of dth-order dependencies where 1≤d≤K, and to combine multiple experts based on the product set using the Bayesian formalism. This framework was experimented and evaluated with a standardized CEN-PARMI data base.

Original languageEnglish
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE
Pages124-129
Number of pages6
Volume2
Publication statusPublished - 1999
EventProceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) - Fort Collins, CO, USA
Duration: 1999 Jun 231999 Jun 25

Other

OtherProceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99)
CityFort Collins, CO, USA
Period99/6/2399/6/25

Fingerprint

Probability distributions

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Software
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Kang, H. J., & Lee, S. W. (1999). Dependency-based framework of combining multiple experts for the recognition of unconstrained handwritten numerals. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 2, pp. 124-129). IEEE.

Dependency-based framework of combining multiple experts for the recognition of unconstrained handwritten numerals. / Kang, Hee Joong; Lee, Seong Whan.

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 2 IEEE, 1999. p. 124-129.

Research output: Chapter in Book/Report/Conference proceedingChapter

Kang, HJ & Lee, SW 1999, Dependency-based framework of combining multiple experts for the recognition of unconstrained handwritten numerals. in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. vol. 2, IEEE, pp. 124-129, Proceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99), Fort Collins, CO, USA, 99/6/23.
Kang HJ, Lee SW. Dependency-based framework of combining multiple experts for the recognition of unconstrained handwritten numerals. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 2. IEEE. 1999. p. 124-129
Kang, Hee Joong ; Lee, Seong Whan. / Dependency-based framework of combining multiple experts for the recognition of unconstrained handwritten numerals. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 2 IEEE, 1999. pp. 124-129
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