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2.8 参考文献

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[2] Mitchell, T. M., Carbonell, J. G., & Michalski, R. S. (2003). Machine Learning:McGraw-Hill.

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[4] Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective: MIT Press.

[5] Wang, Z., & Bovik, A. C. (2009). Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures. IEEE Signal Processing Magazine, 26(1), 98-117.

[6] Akaike, H. (1992). Information Theory and an Extension of the Maximum Likelihood Principle. Inter.symp.on Information Theory, 1, 610-624.

[7] Shore, J. E., & Johnson, R. W. (1980). Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. Information Theory IEEE Transactions on,26(1), 26-37.

[8] Nakama, T. (2009). Theoretical analysis of batch and on-line training for gradient descent learning in neural networks. Neurocomputing, 73(1–3), 151-159.

[9] Li, M., Zhang, T., Chen, Y., & Smola, A. J. (2014). Efficient mini-batch training for stochastic optimization.

[10] Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent:Physica-Verlag HD.

[11] Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2009). Online Learning for Matrix Factorization and Sparse Coding. Journal of Machine Learning Research, 11(1), 19-60.

[12] Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., . . . Sainath, T. N.(2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups. IEEE Signal Processing Magazine, 29(6), 82-97.

[13] Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization.IEEE Transactions on Evolutionary Computation, 1(1), 67-82.

[14] Krizhevsky, A. (2012). Convolutional Deep Belief Networks on CIFAR-10.

[15] Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Paper presented at the International Joint Conference on Artificial Intelligence.

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