How is the Performance of a Neural Network Ensemble influenced by Over-fitting and Ensemble Size - A Numerical Investigation
(2014) FYTK01 20141Computational Biology and Biological Physics - Has been reorganised
- Abstract
- The goal of this investigation is to study the performance of two different aspects of neural network ensembles: one ensemble contains over-trained networks and the other very many members. The over-training of the networks within the first ensemble is achieved with a larger number of hidden neurons than the approximation task in question would require. The result of the investigation is that over-training indeed has the power to improve an ensemble’s performance. The second question, if many ensemble members improve an ensemble’s output, is answered negatively, unless the stability of the prediction of individual input samples has to be enhanced. In this case, it can be said that the larger an ensemble is, the more stable is the... (More)
- The goal of this investigation is to study the performance of two different aspects of neural network ensembles: one ensemble contains over-trained networks and the other very many members. The over-training of the networks within the first ensemble is achieved with a larger number of hidden neurons than the approximation task in question would require. The result of the investigation is that over-training indeed has the power to improve an ensemble’s performance. The second question, if many ensemble members improve an ensemble’s output, is answered negatively, unless the stability of the prediction of individual input samples has to be enhanced. In this case, it can be said that the larger an ensemble is, the more stable is the ensemble’s prediction. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/4465718
- author
- Schwappach, Cordula Sybille LU
- supervisor
- organization
- course
- FYTK01 20141
- year
- 2014
- type
- M2 - Bachelor Degree
- subject
- keywords
- Neural Network Ensemble, Over-Training, Number of Members, Data Analysis
- language
- English
- id
- 4465718
- date added to LUP
- 2014-06-17 07:47:57
- date last changed
- 2020-01-24 12:57:49
@misc{4465718, abstract = {{The goal of this investigation is to study the performance of two different aspects of neural network ensembles: one ensemble contains over-trained networks and the other very many members. The over-training of the networks within the first ensemble is achieved with a larger number of hidden neurons than the approximation task in question would require. The result of the investigation is that over-training indeed has the power to improve an ensemble’s performance. The second question, if many ensemble members improve an ensemble’s output, is answered negatively, unless the stability of the prediction of individual input samples has to be enhanced. In this case, it can be said that the larger an ensemble is, the more stable is the ensemble’s prediction.}}, author = {{Schwappach, Cordula Sybille}}, language = {{eng}}, note = {{Student Paper}}, title = {{How is the Performance of a Neural Network Ensemble influenced by Over-fitting and Ensemble Size - A Numerical Investigation}}, year = {{2014}}, }