Multilayer Perceptron for Prediction of 2006 World Cup Football Game
Joint Authors
Source
Advances in Artificial Neural Systems
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-12-26
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
Multilayer perceptron (MLP) with back-propagation learning rule is adopted to predict the winning rates of two teams according to their official statistical data of 2006 World Cup Football Game at the previous stages.
There are training samples from three classes: win, draw, and loss.
At the new stage, new training samples are selected from the previous stages and are added to the training samples, then we retrain the neural network.
It is a type of on-line learning.
The 8 features are selected with ad hoc choice.
We use the theorem of Mirchandani and Cao to determine the number of hidden nodes.
And after the testing in the learning convergence, the MLP is determined as 8-2-3 model.
The learning rate and momentum coefficient are determined in the cross-learning.
The prediction accuracy achieves 75% if the draw games are excluded.
American Psychological Association (APA)
Huang, Kou-Yuan& Chen, Kai-Ju. 2011. Multilayer Perceptron for Prediction of 2006 World Cup Football Game. Advances in Artificial Neural Systems،Vol. 2011, no. 2011, pp.1-8.
https://search.emarefa.net/detail/BIM-467062
Modern Language Association (MLA)
Huang, Kou-Yuan& Chen, Kai-Ju. Multilayer Perceptron for Prediction of 2006 World Cup Football Game. Advances in Artificial Neural Systems No. 2011 (2011), pp.1-8.
https://search.emarefa.net/detail/BIM-467062
American Medical Association (AMA)
Huang, Kou-Yuan& Chen, Kai-Ju. Multilayer Perceptron for Prediction of 2006 World Cup Football Game. Advances in Artificial Neural Systems. 2011. Vol. 2011, no. 2011, pp.1-8.
https://search.emarefa.net/detail/BIM-467062
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-467062