DocumentCode
1950375
Title
Agnostic Learning with Ensembles of Classifiers
Author
Wichard, Jörg D.
Author_Institution
Inst. of Molecular Pharmacology Molecular Modelling Group, Berlin-Buch
fYear
2007
fDate
12-17 Aug. 2007
Firstpage
2887
Lastpage
2891
Abstract
We present a method for building ensembles of models in order to build proper classifiers. The main advantage of our method is an automated model selection procedure and an automated model parameter estimation. The method is an extension of the classical bagging and the K-fold-cross-validation approach.
Keywords
learning (artificial intelligence); pattern classification; regression analysis; K-fold-cross-validation approach; agnostic learning; automated model parameter estimation; automated model selection procedure; classical bagging approach; ensemble building; pattern classification; regression analysis; Bagging; Biomedical informatics; Decision trees; Neural networks; Parameter estimation; Predictive models; Supervised learning; Training data; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location
Orlando, FL
ISSN
1098-7576
Print_ISBN
978-1-4244-1379-9
Electronic_ISBN
1098-7576
Type
conf
DOI
10.1109/IJCNN.2007.4371418
Filename
4371418
Link To Document