Pattern Classification Using Secondary Components Perceptron and Economic Applications
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.
Volume (Year): 5 (2008)
Issue (Month): 2 (June)
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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Nastac, Iulian & Dobrescu, Emilian & Pelinescu, Elena, 2007. "Neuro-Adaptive Model for Financial Forecasting," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 4(3), pages 19-41, September.
- Ciuiu, Daniel, 2008. "Pattern classification using polynomial and linear regression," MPRA Paper 15355, University Library of Munich, Germany.
- Ciuiu, Daniel & Costinescu, Cristian, 2008. "The Monte Carlo method to find eigenvalues and eigenvectors," MPRA Paper 15362, University Library of Munich, Germany.
- Ciuiu, Daniel, 2008. "Pattern classification using principal components regression," MPRA Paper 15360, University Library of Munich, Germany.
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