1 |
Y. LeCun, Y. Bengio, A. Courville, and G. Hinton, "Deep Learning," Cambridge: MIT Press, 2016.
|
2 |
C. Rich, S. Lawrence, and C. -L. Giles, "Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping," Advances in neural information processing systems, 2001. https://papers.nips.cc/paper/1895-overfitting-in-neural-nets -backpropagation-conjugate-gradient-and-early-stopping. pdf
|
3 |
G. E. Hinton, S. Osindero and Y. W. Teh, "A fast learning algorithm for deep belief nets," Neural computation, 18(7), pp.1527-1554, 2006. https://doi.org/10.1162/neco.2006.18.7.1527
DOI
|
4 |
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, andR. Salakhutdinov, "Dropout: A Simple Way to Prevent Neural Networks from Overfitting," Journal of Machine Learning Research, Vol.15, No.1, pp.1929-1958, 2014. http://jmlr.org/papers/v15/srivastava14a.html
|
5 |
C. Shorten and T. M. Khoshgoftaar, "A survey on Image Data Augmentation for Deep Learning," Journal of Big Data, 6(1), 60, 2019. http://doi.org/10.1186/s40537-019-0197-0
DOI
|
6 |
N. V. Chawla, L. O. Hall, K. W. Bowyer, and W. P. Kegelmeyer, "Smote: Synthetic minority oversampling technique," Journal of Artificial Intelligence Research, Vol.16, pp.321-357, 2002.
DOI
|
7 |
N. S. Altman, "An introduction to kernel and nearestneighbor nonparametric regression," The American Statistician, 46(3), pp.175-185, 1992. http://doi.org/10.1080/00031305.1992.10475879
DOI
|
8 |
S. Hu, Y. Liang, L. Ma and Y. He, "MSMOTE: improving classification performance when training data is imbalanced," 2009 second international workshop on computer science and engineering, Vol.2, pp.13-17, 2009.
|
9 |
Lim, J. S., Oh, Y. S., & Lim, D. H, "Bagging support vector machine for improving breast cancer classification," J Health Info Stat, 39(1), pp.15-24. 2014. https://e-jhis.org/journal/view.php?number=426
|
10 |
H. Cao, X.-L. Li, D.-K. Woon, and S.-K. Ng, "Integrated oversampling for imbalanced time series classification," IEEE Trans. Knowl. Data Eng., vol. 25, no. 12, pp.2809-282, Dec. 2013. https://doi.org/10.1109/TKDE.2013.37
DOI
|
11 |
G. E. Hinton and R. R. Salakhutdinov, "Reducing the Dimensionality of Data with Neural Networks," Science, Vol. 313, no. 5786, pp.504-507, 2006. https://doi.org/10.1126/science.1127647
DOI
|
12 |
S. Wold, K. Esbensen, and P. Geladi, "Principal component analysis," Chemometrics and intelligent laboratory systems, 2(1-3), pp.37-52. 1987.
DOI
|
13 |
H. Abdi, "The eigen-decomposition: Eigenvalues and eigenvectors," Encyclopedia of measurement and statistics, pp.304-308, 2007. https://personal.utdallas.edu/-herve/Abdi-EVD2007-pretty.pdf
|
14 |
P. Vincent, H. Larochelle, Y. Bengio and P. A. Manzagol, "Extracting and composing robust features with denoising autoencoders," In Proceedings of the 25th international conference on Machine learning, pp.1096-1103, 2008.
|
15 |
S. Hochreiter, The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6(02), pp.107-116, 1998.
DOI
|
16 |
Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, "Greedy Layer-Wise Training of Deep Networks," Adv. Neural Inf. Process. Syst., Vol. 19, no. 1, pp. 153-160, 2007.
|
17 |
Finney, D. John, "Probit analysis: a statistical treatment of the sigmoid response curve," Cambridge university press, Cambridge, 1952.
|
18 |
UCI Machine Learning Repository. University of California, Center for Machine Learning and Intelligent Systems. Available at https://archive.ics.uci.edu/ml/datasets.php
|