Title :
A comparative study of statistical ensemble methods on mismatch conditions
Author :
Luo, Dingsheng ; Chen, Ke
Author_Institution :
Nat. Lab. on Machine Perception, Peking Univ., Beijing, China
fDate :
6/24/1905 12:00:00 AM
Abstract :
Unlike previous comparative studies, we present an empirical evaluation on three typical statistical ensemble methods - boosting, bagging and combination of weak perceptrons - in terms of speaker identification where miscellaneous mismatch conditions are involved. During creating an ensemble, moreover, different combination strategies are also investigated. As a result, our studies present their generalization capabilities on mismatch conditions, which provides an alternative insight to understand those methods
Keywords :
generalisation (artificial intelligence); pattern classification; perceptrons; speaker recognition; statistical analysis; vector quantisation; generalization; mismatch conditions; pattern classification; speaker identification; statistical ensemble; vector quantization; weak perceptrons; Aging; Bagging; Boosting; Computer science; Databases; Information science; Laboratories; Learning systems; Mood; Training data;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1005442