Nonlinear parameter estimation: an integrated system in BASIC
Nonlinear parameter estimation: an integrated system in BASIC
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Kolmogorov's theorem and multilayer neural networks
Neural Networks
Self-organization as an iterative kernel smoothing process
Neural Computation
Model selection in neural networks
Neural Networks
A bibliography of neural network business applications research: 1994–1998
Computers and Operations Research - Neural networks in business
Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigms
Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigms
Computational Methods in the Chemical Sciences
Computational Methods in the Chemical Sciences
Capabilities of a four-layered feedforward neural network: four layers versus three
IEEE Transactions on Neural Networks
Computers and Operations Research
Computers and Operations Research
Determinants of house prices in Turkey: Hedonic regression versus artificial neural network
Expert Systems with Applications: An International Journal
Estimation of Rock Mass Rating System with an Artificial Neural Network
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Expert Systems with Applications: An International Journal
Bare ownership evaluation. Hedonic price model vs. artificial neural network
International Journal of Business Intelligence and Data Mining
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This paper examines the potential of a neural network (NN) approach to the analysis of'hedonic' regressions, in which price is dependent on quality characteristics. The aim of the regressions is to measure, using objective data, the valuation consumers place on these characteristics. A neural network approach is employed because of potential non-linearities in the hedonic functions, using the property of 'universal approximation'. Our NN implementation goes beyond the now-orthodox approach in using the Polytope algorithm, which we compare with Backpropagation, and uses two hidden layers. The results obtained provide an improvement on linear formulations, but the improvement in this case is relatively marginal. We view NN modelling as a useful means of specification testing and hence our results imply some support for a linear formulation as an adequate approximation. From a managerial perspective, the linear model is more easily interpreted. NN modelling is potentially very time-consuming, especially with the Polytope algorithm, and requires a good deal of technical skill.