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Pattern Classification Using Secondary Components Perceptron and Economic Applications

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Author Info
Ciuiu, Daniel () (Technical University of Civil Engineering, Bucharest, Romania)

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Abstract

In this paper we will classify patterns using a modified Perceptron algorithm (Dumitrache et al., 1999). The generalization uses the eigenvalues and the eigenvectors of the sample covariance matrix, as we did for classifying patterns using PCR (Ciuiu 2007b).We shall also define measurements for the cohesion of the obtained classes and of the separation between them. The first economic application considered in the paper is a consumer behavior model (Jula 2003), and the second is the same financial application for classifying banks (Ciuiu, 2007a, Ciuiu, 2007b), where we have used regression for classification.

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File URL: http://www.ipe.ro/rjef/rjef2_08/rjef2_08_4.pdf
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Publisher Info
Article provided by Institute for Economic Forecasting in its journal Romanian Journal for Economic Forecasting.

Volume (Year): 5 (2008)
Issue (Month): 2 (June)
Pages: 51-66
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Handle: RePEc:rjr:romjef:v:5:y:2008:i:2:p:51-66

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Related research
Keywords: Perceptron; principal and secondary components; consumption; banks;

Find related papers by JEL classification:
C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
E21 - Macroeconomics and Monetary Economics - - Macroeconomics: Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Mortgages

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This page was last updated on 2009-11-17.

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