A hybrid neural network for input that is both categorical and quantitative

International Journal of Intelligent Systems

Volumn 19, Issue 10, 2004, Pages 979-1001

  Brouwer, Roelof K ^a  

^a THOMPSON RIVERS UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

FUNCTIONS; MATHEMATICAL MODELS; MATRIX ALGEBRA; PATTERN RECOGNITION; PROBLEM SOLVING;

CONTINUOUS FUNCTIONS; HYBRID NETWORKS; SIGMOID FUNCTIONS;

MULTILAYER NEURAL NETWORKS;

EID: 5444241181     PISSN: 08848173     EISSN: None     Source Type: Journal    
DOI: 10.1002/int.20032     Document Type: Article

References (12)

1. Harrison D, Rubinfeld DL. Hedonic prices and the demand for clean air. J Environ Econ Manag 1978;5:81-102. The data set is available at http://www.ics.uci.edu/~mlearn/MLRepository.html.
2. Marine Resources Division, Marine Research Laboratories, Taroona, Department of Primary Industry and Fisheries, Tasmania GPO Box 619F, Hobart, Tasmania 7001, Australia, available at http://www.ics.uci.edu/~mlearn/ MLRepository.html.
3. Werbos PJ. Beyond regression: New tools for prediction and analysis in the behavioral sciences. Doctoral Dissertation, Harvard University. 1974.
4. Rumelhart DE, Hinton GE, McClelland JL. A general framework for parallel distributed processing. In: Rumelhart DE, McClelland JL, and the PDF Research Group, editors. Parallel distributed processing. Cambridge, MA: The MIT Press; 1986. pp 45-76.
5. Jacobs RA, Jordan ML, Nowlan SJ, Hinton GE. Adaptive mixtures of local experts. Neural Comput 1992;3:79-87.
6. Barton JG, Lees A. Comparison of shoe insole materials by neural network analysis. Med Biol Eng Comput 1996;34:453-459.
7. Burgess AN. Non-linear model identification and statistical significance tests and their application to financial modeling. In: 4th Int Conf on Artificial Neural Networks. Cambridge, UK; 1995.
8. Bishop CM. Mixture Density Networks. NCRG/94/004, available at http://www.ncrg.aston.ac.uk.
9. Bishop CM. Neural Networks for Pattern Recognition. Oxford: Clarendon Press; 1995.
10. Lee K, Lee T. Design of neural networks for multi-value regession. In: Int Joint Conf Neural Networks; 2001. pp 93-98.
11. Iverson KE. Elementary functions: An algorithmic treatment. Chicago: Science Research Associates; 1966.
12. Iverson KE. J Introduction and Dictionary. Iverson Software Incorporated; 1996.