Title :
Agnostic Learning with Ensembles of Classifiers
Author :
Wichard, Jörg D.
Author_Institution :
Inst. of Molecular Pharmacology Molecular Modelling Group, Berlin-Buch
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;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4371418
