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A STUDY OF EARLY STOPPING AND MODEL SELECTION APPLIED TO THE PAPERMAKING INDUSTRY
- Source :
- International Journal of Neural Systems. 10:9-18
- Publication Year :
- 2000
- Publisher :
- World Scientific Pub Co Pte Lt, 2000.
-
Abstract
- This paper addresses the issues of neural network model development and maintenance in the context of a complex task taken from the papermaking industry. In particular, it describes a comparison study of early stopping techniques and model selection, both to optimise neural network models for generalisation performance. The results presented here show that early stopping via use of a Bayesian model evidence measure is a viable way of optimising performance while also making maximum use of all the data. In addition, they show that ten-fold cross-validation performs well as a model selector and as an estimator of prediction accuracy. These results are important in that they show how neural network models may be optimally trained and selected for highly complex industrial tasks where the data are noisy and limited in number.
- Subjects :
- Paper
Measure (data warehouse)
Early stopping
Artificial neural network
Computer Networks and Communications
Computer science
business.industry
Model selection
Estimator
Context (language use)
General Medicine
Models, Theoretical
computer.software_genre
Bayesian inference
Machine learning
Task (project management)
Industry
Neural Networks, Computer
Data mining
Artificial intelligence
business
computer
Algorithms
Subjects
Details
- ISSN :
- 17936462 and 01290657
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- International Journal of Neural Systems
- Accession number :
- edsair.doi.dedup.....bf89fe25301466b549c50247d06757ed