DocumentCode :
2402951
Title :
Nonlinear committee pattern classification
Author :
Hu, Yu Hen ; Knoblock, Thomas ; Park, Jong-Ming
Author_Institution :
Dept. of Electr. & Comput. Eng., Wisconsin Univ., Madison, WI, USA
fYear :
1997
fDate :
24-26 Sep 1997
Firstpage :
568
Lastpage :
577
Abstract :
Methods which combine outputs of multiple pattern classifiers to enhance the overall classification rate are studied. Specific attention is given to combination rules which are independent of the input feature vectors. Potential performance enhancement and limits of this so called stack generalization method are discussed. In particular, a phenomenon called “alias” is introduced which gives an upper bound of the performance which can be achieved using stack generation for a given set of member classifiers. Experimentation using several machine learning databases are reported
Keywords :
generalisation (artificial intelligence); learning (artificial intelligence); pattern classification; probability; alias; classification rate; machine learning databases; nonlinear committee pattern classification; performance enhancement; stack generalization method; Artificial intelligence; Decision making; Fuzzy logic; Image recognition; Pattern classification; Pattern recognition; Signal processing algorithms; Target recognition; Upper bound; Voting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop
Conference_Location :
Amelia Island, FL
ISSN :
1089-3555
Print_ISBN :
0-7803-4256-9
Type :
conf
DOI :
10.1109/NNSP.1997.622439
Filename :
622439
Link To Document :
بازگشت