Abstract
An ensemble of GM-RVFL networks is applied to the prediction of housing prices in the Boston metropolitan area on the basis of various socio-economic explanatory variables. The ARD scheme is tested and found to succeed in identifying and effectively switching off two redundant dummy inputs added to the data. The employment of a network committee leads to significantly better results than achieved with an individual network. A simple Bayesian regularisation scheme is applied, but found to decrease only the generalisation ‘error’ of the single-model predictor. For a committee, the best generalisation performance is achieved when employing over-complex, under-regularised models that, individually, overfit the training data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag London Limited
About this chapter
Cite this chapter
Husmeier, D. (1999). A Real-World Application: The Boston Housing Data. In: Neural Networks for Conditional Probability Estimation. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0847-4_16
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0847-4_16
Publisher Name: Springer, London
Print ISBN: 978-1-85233-095-8
Online ISBN: 978-1-4471-0847-4
eBook Packages: Springer Book Archive