TY - GEN
T1 - Product approximation by minimizing the upper bound of bayes error rate for bayesian combination of classifiers
AU - Kang, Hee Joong
AU - Doermann, David
PY - 2004
Y1 - 2004
N2 - In combining multiple classifiers using a Bayesian formalism, a high dimensional probability distribution is composed of a class and decisions of classifiers. In order to do product approximation of the probability distribution, the upper bound of Bayes error rate, bounded by the conditional entropy of a class and decisions, should be minimized. A second-order dependency-based product approximation is proposed in this paper by considering the second-order dependency between the class and decisions. The proposed method is evaluated by combining the classifiers recognizing unconstrained handwritten numerals.
AB - In combining multiple classifiers using a Bayesian formalism, a high dimensional probability distribution is composed of a class and decisions of classifiers. In order to do product approximation of the probability distribution, the upper bound of Bayes error rate, bounded by the conditional entropy of a class and decisions, should be minimized. A second-order dependency-based product approximation is proposed in this paper by considering the second-order dependency between the class and decisions. The proposed method is evaluated by combining the classifiers recognizing unconstrained handwritten numerals.
UR - https://www.scopus.com/pages/publications/10044273942
U2 - 10.1109/ICPR.2004.1334071
DO - 10.1109/ICPR.2004.1334071
M3 - Conference contribution
AN - SCOPUS:10044273942
SN - 0769521282
T3 - Proceedings - International Conference on Pattern Recognition
SP - 252
EP - 255
BT - Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
A2 - Kittler, J.
A2 - Petrou, M.
A2 - Nixon, M.
T2 - Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
Y2 - 23 August 2004 through 26 August 2004
ER -