Tài liệu tham khảo |
Loại |
Chi tiết |
[1] R. Solomonoff, “An Inductive Inference Machine,” IRE Conv. Rec., vol. Section on, 1957 |
Sách, tạp chí |
Tiêu đề: |
An Inductive Inference Machine,” "IRE Conv. Rec |
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[2] H. Rogers, Theory of Recursive Functions and Effective Computability. 1987 |
Sách, tạp chí |
Tiêu đề: |
Theory of Recursive Functions and Effective Computability |
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[3] A. L. Samuel, “Some Studies in Machine Learning Using the Game of Checkers,” IBM Journal of Research and Development, vol. 3. pp. 210–229, 1959 |
Sách, tạp chí |
Tiêu đề: |
Some Studies in Machine Learning Using the Game of Checkers,” "IBM Journal of Research and Development |
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[4] C. M. Bishop, Pattern Recognition and Machine Learning, vol. 4. 2006 |
Sách, tạp chí |
Tiêu đề: |
Pattern Recognition and Machine Learning |
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Sách, tạp chí |
Tiêu đề: |
Neural network studies. 1. Comparison of overfitting and overtraining,” "J. Chem. Inf. Comput. Sci |
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[7] C. P. M. R. Greene D., “Unsupervised learning and clustering,” Lect. Notes Appl. Comput. Mech., pp. 51–90, 2008 |
Sách, tạp chí |
Tiêu đề: |
Unsupervised learning and clustering,” "Lect. Notes Appl. "Comput. Mech |
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[8] H. B. Barlow, “Unsupervised Learning,” Neural Computation, vol. 1. pp. 295–311, 1989 |
Sách, tạp chí |
Tiêu đề: |
Unsupervised Learning,” "Neural Computation |
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[9] T. J. S. Geoffrey E. Hinton, Unsupervised Learning: Foundations of Neural Computation. 1999 |
Sách, tạp chí |
Tiêu đề: |
Unsupervised Learning: Foundations of Neural Computation |
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[10] V. N. Vapnik, The Nature of Statistical Learning Theory, vol. 8. 1995 |
Sách, tạp chí |
Tiêu đề: |
The Nature of Statistical Learning Theory |
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[11] R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Ann. Hum. Genet., vol. 7, pp. 179–188, 1936 |
Sách, tạp chí |
Tiêu đề: |
The use of multiple measurements in taxonomic problems,” "Ann. "Hum. Genet |
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[13] S. L. Gortmaker, D. W. Hosmer, and S. Lemeshow, “Applied Logistic Regression.,” Contemporary Sociology, vol. 23. p. 159, 1994 |
Sách, tạp chí |
Tiêu đề: |
Applied Logistic Regression.,” "Contemporary Sociology |
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[14] V. Bewick, L. Cheek, and J. Ball, “Statistics review 14: Logistic regression.,” Crit. Care, vol. 9, pp. 112–118, 2005 |
Sách, tạp chí |
Tiêu đề: |
Statistics review 14: Logistic regression.,” "Crit. "Care |
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[15] C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, Sep. 1995 |
Sách, tạp chí |
Tiêu đề: |
Support-vector networks,” "Mach. Learn |
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[16] Tin Kam Ho, “Random decision forests,” in Proceedings of 3rd International Conference on Document Analysis and Recognition, 1995, vol. 1, pp. 278–282 |
Sách, tạp chí |
Tiêu đề: |
Random decision forests,” in "Proceedings of 3rd International Conference on Document Analysis and Recognition |
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[17] L. Breiman, “Random Forests,” Mach. Learn., vol. 45, pp. 5–32, 2001 |
Sách, tạp chí |
Tiêu đề: |
Random Forests,” "Mach. Learn |
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[18] I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Google eBook). 2005 |
Sách, tạp chí |
Tiêu đề: |
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Google eBook) |
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[19] I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” J. Mach. Learn. Res., vol. 3, pp. 1157–1182, Mar. 2003 |
Sách, tạp chí |
Tiêu đề: |
An introduction to variable and feature selection,” "J. "Mach. Learn. Res |
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[20] R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural Features for Image Classification,” IEEE Trans. Syst. Man. Cybern., vol. 3, no. 6, pp. 610–621, Nov.1973 |
Sách, tạp chí |
Tiêu đề: |
Textural Features for Image Classification,” "IEEE Trans. Syst. Man. Cybern |
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[22] D. Rumelhart, G. Hinton, and R. Williams, “Learning internal representations by error propagation,” 1985 |
Sách, tạp chí |
Tiêu đề: |
Learning internal representations by error propagation |
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[23] Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, “Greedy Layer-Wise Training of Deep Networks,” Adv. Neural Inf. Process. Syst. 19, pp. 153–160, 2007 |
Sách, tạp chí |
Tiêu đề: |
Greedy Layer-Wise Training of Deep Networks,” "Adv. Neural Inf. Process. Syst. 19 |
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