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Chi-Jie Lu
Researcher at Fu Jen Catholic University
Publications - 80
Citations - 3200
Chi-Jie Lu is an academic researcher from Fu Jen Catholic University. The author has contributed to research in topics: Support vector machine & Artificial neural network. The author has an hindex of 25, co-authored 75 publications receiving 2674 citations. Previous affiliations of Chi-Jie Lu include Chien Hsin University of Science and Technology & Yuan Ze University.
Papers
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Financial time series forecasting using independent component analysis and support vector regression
TL;DR: Experimental results show that the proposed model outperforms the SVR model with non-filtered forecasting variables and a random walk model.
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Mining the customer credit using classification and regression tree and multivariate adaptive regression splines
TL;DR: As the results reveal, CART and MARS outperform traditional discriminant analysis, logistic regression, neural networks, and support vector machine (SVM) approaches in terms of credit scoring accuracy and hence provide efficient alternatives in implementing credit scoring tasks.
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Credit scoring using the hybrid neural discriminant technique
TL;DR: The objective of the proposed study is to explore the performance of credit scoring by integrating the backpropagation neural networks with traditional discriminant analysis approach, and the proposed hybrid approach converges much faster than the conventional neural networks model.
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A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting
TL;DR: A new stock price forecasting model which integrates wavelet transform, multivariate adaptive regression splines (MARS), and support vector regression (SVR) is proposed to not only address the problem of wavelet sub-series selection but also improve the forecast accuracy.
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Automatic defect inspection for LCDs using singular value decomposition
Chi-Jie Lu,Du-Ming Tsai +1 more
TL;DR: In this article, a global image reconstruction scheme using the singular value decomposition (SVD) is proposed to eliminate periodical, repetitive patterns of the textured image, and preserve the anomalies in the restored image.