Neural Networks in Economics

作者: Ralf Herbrich , Max Keilbach , Thore Graepel , Peter Bollmann-Sdorra , Klaus Obermayer

DOI: 10.1007/978-1-4615-5029-7_7

关键词:

摘要: Neural Networks – originally inspired from Neuroscience provide powerful models for statistical data analysis. Their most prominent feature is their ability to “learn” dependencies based on a finite number of observations. In the context term “learning” means that knowledge acquired samples can be generalized as yet unseen this sense, Network often called Learning Machine. As such, might considered metaphor an agent who learns his environment and thus infers strategies behavior limited contribution, however, we want abstract biological origins rather present them purely mathematical model.

参考文章(78)
Robert A. Marose, A financial neural-network application AI Expert archive. ,vol. 5, pp. 50- 53 ,(1990)
B. D. Ripley, Neural Networks and Related Methods for Classification Journal of the Royal Statistical Society: Series B (Methodological). ,vol. 56, pp. 409- 437 ,(1994) , 10.1111/J.2517-6161.1994.TB01990.X
B Schölkopf, Support vector learning Oldenbourg München, Germany. ,(1997)
Corinna Cortes, Prediction of Generalization Ability in Learning Machines University of Rochester. ,(1994)
David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams, Learning representations by back-propagating errors Nature. ,vol. 323, pp. 696- 699 ,(1988) , 10.1038/323533A0
D. Michie, C. Feng, Machine learning of rules and trees Machine learning, neural and statistical classification. pp. 50- 83 ,(1995)
Clive W. J. Granger, Developments in the Nonlinear Analysis of Economic Series The Scandinavian Journal of Economics. ,vol. 93, pp. 263- 276 ,(1991) , 10.2307/3440334
David E. Rumelhart, Andreas S. Weigend, Bernardo A. Huberman, Predicting sunspots and exchange rates with connectionist networks ,(1991)
Pietro Terna, Andrea Beltratti, Sergio Margarita, Neural Networks for Economic and Financial Modelling ,(1995)