Reference: | [1] Agatonovic-Kustrin, S., & Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of pharmaceutical and biomedical analysis, 22(5), 717-727. [2] Tsaih, R. R. (1998). An explanation of reasoning neural networks. Mathematical and Computer Modelling, 28(2), 37-44. [3] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. [4] Xue, Y. (2019, February). An Overview of Overfitting and its Solutions. In Journal of Physics: Conference Series (Vol. 1168, No. 2, p. 022022). IOP Publishing. [5] Tsaih, R. H., & Cheng, T. C. (2009). A resistant learning procedure for coping with outliers. Annals of Mathematics and Artificial Intelligence, 57(2), 161-180. [6] Chang, H.Y. (2019). The sequentially-learning-based algorithm and the prediction of the turning points of bull and bear markets (Master’s dissertation). National Chengchi University, 1-39. [7] Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559-572. [8] Smith, L. I. (2002). A tutorial on principal components analysis. [9] Shlens, J. (2014). A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100. [10] Hanna, A. J. (2018). A top-down approach to identifying bull and bear market states. International Review of Financial Analysis, 55, 93-110. [11] Pagan, A. R., & Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal of applied econometrics, 18(1), 23-46. [12] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929-1958. [13] Talathi, S. S., & Vartak, A. (2015). Improving performance of recurrent neural network with relu nonlinearity. arXiv preprint arXiv:1511.03771. [14] Tsaih, R. R. (1993). The softening learning procedure. Mathematical and computer modelling, 18(8), 61-64. [15] Allamy, H. (2014). Methods to Avoid Over-Fitting and Under-Fitting in Supervised Machine Learning (Comparative Study). Computer Science, Communication and Instrumentation Devices, Kochi, India (December 27, 2014). [16] Caruana, R., Lawrence, S., & Giles, C. L. (2001). Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping. In Advances in neural information processing systems (pp. 402-408). [17] Cawley, G. C. (2012, October). Over-Fitting in Model Selection and Its Avoidance. In IDA (p. 1). [18] Chauvin, Y. (1989). A back-propagation algorithm with optimal use of hidden units. In Advances in neural information processing systems (pp. 519-526). [19] Ishikawa, M. (1989). A structural learning algorithm with forgetting of link weights. In International 1989 Joint Conference on Neural Networks (pp. 626-vol). IEEE. [20] Weigend, A. S., Rumelhart, D. E., & Huberman, B. A. (1991). Generalization by weight-elimination with application to forecasting. In Advances in neural information processing systems (pp. 875-882). [21] Krogh, A., & Hertz, J. A. (1992). A simple weight decay can improve generalization. In Advances in neural information processing systems (pp. 950-957). [22] LeCun, Y., Denker, J. S., & Solla, S. A. (1990). Optimal brain damage. In Advances in neural information processing systems (pp. 598-605). [23] Srivastava, N. (2013). Improving neural networks with dropout. University of Toronto, 182(566), 7. [24] Jackson, J. E. (2005). A user`s guide to principal components (Vol. 587). John Wiley & Sons. (pp. 1-3) [25] Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of educational psychology, 24(6), 417. [26] Fisher, R. A., & Mackenzie, W. A. (1923). Studies in crop variation. II. The manurial response of different potato varieties. The Journal of Agricultural Science, 13(3), 311-320. [27] Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1-3), 37-52. [28] Tripathi, A. (2019), A Complete Guide to Principal Component Analysis – PCA in Machine earning. URL“https://towardsdatascience.com/a-complete-guide-to- principal-component-analysis-pca-in-machine-learning-664f34fc3e5a” [29] Jolliffe, I. T. (2002). Principal component analysis. [30] Xu, X., & Wen, C. (2017). Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis. Journal of Control Science and Engineering, 2017. [31] Kashani, M. N., Aminian, J., Shahhosseini, S., & Farrokhi, M. (2012). Dynamic crude oil fouling prediction in industrial preheaters using optimized ANN based moving window technique. Chemical Engineering Research and Design, 90(7), 938-949. [32] Chen, S. S. (2009). Predicting the bear stock market: Macroeconomic variables as leading indicators. Journal of Banking & Finance, 33(2), 211-223. [33] Chen, S. S. (2012). Revisiting the empirical linkages between stock returns and trading volume. Journal of Banking & Finance, 36(6), 1781-1788. |