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Balanced bootstrap resampling method for neural model selection
- Source :
- Computers & Mathematics with Applications. 62:4576-4581
- Publication Year :
- 2011
- Publisher :
- Elsevier BV, 2011.
-
Abstract
- Uniform resampling is the easiest to apply and is a general recipe for all problems, but it may require a large replication size B. To save computational effort in uniform resampling, balanced bootstrap resampling is proposed to change the bootstrap resampling plan. This resampling plan is effective for approximating the center of the bootstrap distribution. Therefore, this paper applies it to neural model selection. Numerical experiments indicate that it is possible to considerably reduce the replication size B. Moreover, the efficiency of balanced bootstrap resampling is also discussed in this paper.
- Subjects :
- Computer science
business.industry
Model selection
Machine learning
computer.software_genre
Bootstrap distribution
Bootstrap
Computational Mathematics
Computational Theory and Mathematics
Backpropagation multilayer perceptron
Modelling and Simulation
Modeling and Simulation
Resampling
Replication (statistics)
Balanced resampling
Artificial intelligence
business
computer
Jackknife resampling
Algorithm
Subjects
Details
- ISSN :
- 08981221
- Volume :
- 62
- Database :
- OpenAIRE
- Journal :
- Computers & Mathematics with Applications
- Accession number :
- edsair.doi.dedup.....6035dc329d8f456e636a48e204a0b082