DocumentCode :
1566301
Title :
Data mining - an adaptive neural network model for financial analysis
Author :
Xu, Shuxiang ; Zhang, Ming
Author_Institution :
Sch. of Comput., Tasmania Univ., Launceston, Tas., Australia
Volume :
1
fYear :
2005
Firstpage :
336
Abstract :
Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. One of the most commonly used techniques in data mining, artificial neural networks provide nonlinear predictive models that learn through training and resemble biological neural networks in structure. This paper deals with a new adaptive neural network model: a feed-forward neural network with a new activation function called neuron-adaptive activation function. Experiments with function approximation and stock market movement analysis have been conducted to justify the new adaptive neural network model. Experimental results have revealed that the new adaptive neural network model presents several advantages over traditional neuron-fixed feed-forward networks such as much reduced network size, faster learning, and more promising financial analysis.
Keywords :
data mining; data warehouses; feedforward neural nets; financial management; adaptive neural network model; artificial neural networks; biological neural networks; data mining; data warehouse; database; feed-forward neural network; financial analysis; function approximation; hidden predictive information extraction; neuron-adaptive activation function; neuron-fixed feed-forward networks; nonlinear predictive model; stock market movement analysis; Adaptive systems; Artificial neural networks; Biological system modeling; Data mining; Data warehouses; Databases; Feedforward neural networks; Feedforward systems; Neural networks; Predictive models;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Technology and Applications, 2005. ICITA 2005. Third International Conference on
Print_ISBN :
0-7695-2316-1
Type :
conf
DOI :
10.1109/ICITA.2005.109
Filename :
1488822
Link To Document :
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