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Improved Backpropagation Model for Classification of Profitability Analysis Data : A Study on Malaysian Market

Paper Type	Contributed Paper
Title	Improved Backpropagation Model for Classification of Profitability Analysis Data : A Study on Malaysian Market
Author	Siti Mariyam Hj. Shamsuddin* [a], Saiful Hafizah Hj. Jaaman [b], Noriza Majid [b] and Noriszura Ismail [b] and Noriszura I
Email	mariyam@fsksm.utm.my
Abstract: In Malaysian’s economy there are many factors needed to be considered in order to maintain the economic growth. For instance, prices, interest rates, and employment level always react with each other in the form of nonlinear relationship. In a nonlinear system, the effect depends on the values of other inputs, thus the relationship is a higher-order function. Neural network (NN) is an approach that can cater nonlinear problems, and an implementation of an algorithm inspired by research into the brain. NN is a technology in which computer learns directly from data, thereby assisting in classification, function estimation, data compression, and similar tasks. In this paper, we introduce neural network model with an improved backpropagation error function for predicting profitability of selected firms at Kuala Lumpur Stock Exchange (KLSE). The results obtained are compared to the standard backpropagation model with mean square error function (MSE).
Start & End Page	35 - 41
Received Date	2001-06-26
Revised Date
Accepted Date	2002-03-01
Full Text	Download
Keyword	neural network, improved backpropagation, profitability, mean square error
Volume	Vol.29 No.1 (APRIL 2002)
DOI
Citation	[a] S.M.H.S., [b] S.H.H.J., [b] N.M. and I N.I.[.N., Improved Backpropagation Model for Classification of Profitability Analysis Data : A Study on Malaysian Market, Chiang Mai Journal of Science, 2002; 29(1): 35-41.
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Improved Backpropagation Model for Classification of Profitability Analysis Data : A Study on Malaysian Market

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