DocumentCode
2288338
Title
Predictive performance for the detection of underfitting in density estimation
Author
Sardo, Lucia ; Kittler, Josef
Author_Institution
Sch. of Electron. Eng. & Inf. Technol & Math., Surrey Univ., Guildford, UK
fYear
1997
fDate
7-9 Jul 1997
Firstpage
24
Lastpage
29
Abstract
A methodology for model selection and validation is presented. The technique is applied to probability density estimation using Radial Basis Function (RBF) Neural Network. A model is assumed to have a good-fit, if it is able to predict the data. We present a procedure to test if the prediction of the model is calibrated, i.e. if the predicted data frequencies match the empirical data frequencies. Some experimental results show the benefits of such an approach
Keywords
feedforward neural nets; Radial Basis Function; density estimation; model selection; prediction; probability density estimation; underfitting;
fLanguage
English
Publisher
iet
Conference_Titel
Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440)
Conference_Location
Cambridge
ISSN
0537-9989
Print_ISBN
0-85296-690-3
Type
conf
DOI
10.1049/cp:19970696
Filename
607487
Link To Document