References
- Nyberg H. Forecasting the direction of the US stock market with dynamic binary probit models. Int J Forecast. 2011;27(2):561–578.
- Ballings M, Poel DVD, Hespeels N, et al. Evaluating multiple classifiers for stock price direction prediction. Expert Syst Appl. 2015;42(20):7046–7056.
- Thein HTT, Tun KMM. An approach for breast cancer diagnosis classification using neural network. Adv Comput: Int J. 2015;6(1):1–11.
- Burke HB, Goodman PH, Rosen DB, et al. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer. 1997;79(4):857.
- Kim E, Kim W, Lee Y. Combination of multiple classifiers for the customer's purchase behavior prediction. Decis Support Syst. 2003;34(2):167–175.
- Berry MJ, Linoff G. Data mining techniques: for marketing, sales, and customer support. Indianapolis: John Wiley & Sons; 1997.
- Hastie T, Friedman J, Tibshirani R. The elements of statistical learning. 2nd ed. New York (NY): Springer; 2009.
- Vapnik V. The nature of statistical learning theory. New York (NY): Springer; 1999.
- Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence, Montreal; 1995;1137–1143.
- Sonnenburg S, Rätsch G, Schäfer C, et al. Large scale multiple kernel learning. J Mach Learn Res. 2006;7:1531–1565.
- GÖnen M. Bayesian efficient multiple kernel learning. Proceedings of the 29th International Conference on Machine Learning, Edinburgh; 2012;1-8.
- GÖnen M, Alpaydın E. Multiple kernel learning algorithms. J Mach Learn Res. 2011;12:2211–2268.
- Beal MJ. Variational algorithms for approximate Bayesian inference [PhD thesis]. London (UK): The Gatsby Computational Neuroscience Unit, University College London; 2003.
- Murphy KP. Machine learning: a probabilistic perspective. Cambridge: MIT Press; 2012.
- Lawrence ND, Jordan MI. Semi-supervised learning via Gaussian processes. In Advances in Neural Information Processing Systems, Vancouver; 2005;753-760.
- Tipping ME. The relevance vector machine. Adv Neural Inf Process Syst. 2000;12:652–658.
- Parisi G. Statistical field theory. Redwood: Addison-Wesley; 1988.
- Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge: MIT Press; 2016.
- Mclachlan GJ, Krishnan T. The EM algorithm and extensions. 2nd ed. Hoboken: John Wiley & Sons; 2007.
- Dua D, Graff C. UCI machine learning repository. http://archive.ics.uci.edu/ml. Irvine, CA: University of California, School of Information and Computer Science; 2019.
- Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Trans Intel Syst Technol. 2011;2(3):27.
- Zhang XL, Begleiter H, Porjesz B, et al. Event related potentials during object recognition tasks. Brain Res Bull. 1995;38(6):531–8.
- Bing L, Min KK, Altman N. On dimension folding of matrix- or array-valued statistical objects. Ann Stat. 2010;38(2):1094–1121.
- Liu Y, Zheng YF. One-against-all multi-class SVM classification using reliability measures. 2005 IEEE International Joint Conference on Neural Networks, Montreal; 2005;2:849-854.
- Galar M, Fernández A, Barrenechea E, et al. An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recognit. 2011;44(8):1761–1776.
- Ganganwar V. An overview of classification algorithms for imbalanced datasets. Int J Emerg Technol Adv Eng. 2012;2(4):42–47.