### 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 language | English |
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Title of host publication | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |

Publisher | IEEE |

Pages | 124-129 |

Number of pages | 6 |

Volume | 2 |

Publication status | Published - 1999 |

Event | Proceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) - Fort Collins, CO, USA Duration: 1999 Jun 23 → 1999 Jun 25 |

### Other

Other | Proceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) |
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City | Fort Collins, CO, USA |

Period | 99/6/23 → 99/6/25 |

### Fingerprint

### ASJC Scopus subject areas

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

### Cite this

*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.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

*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.

}

TY - CHAP

T1 - Dependency-based framework of combining multiple experts for the recognition of unconstrained handwritten numerals

AU - Kang, Hee Joong

AU - Lee, Seong Whan

PY - 1999

Y1 - 1999

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0032638722&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032638722&partnerID=8YFLogxK

M3 - Chapter

VL - 2

SP - 124

EP - 129

BT - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

PB - IEEE

ER -