Appendix A. Bibliography

The following are the references for all the citations throughout the book:

  • Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J. L. (2013). A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges (Belgium), 24-26 April 2013.
  • Bengio, Y. (2012). Practical Recommendations for Gradient-Based Training of Deep Architectures. In Neural Networks: Tricks of the Trade (pp. 437-478). Springer Berlin Heidelberg. (Also on the arXiv: http://arxiv.org/pdf/1206.5533.pdf)
  • Bengio, Y., Courville, A., and Vincent, P. (2013). Representation Learning: A Review and New Perspectives. Pattern Analysis and Machine Intelligence, IEEE Transactions, 35(8), 1798-1828.
  • Bergmeir, C., and Benítez, J. M. (2012). Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS. Journal of Statistical Software, 46(7), 1-26.
  • Bishop, C. M. (2006). Pattern Recognition and Machine Learning, Springer.
  • Goodfellow, I. J., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y. (2013). Maxout Networks. arXiv preprint arXiv:1302.4389.
  • Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition. Springer.
  • Hinton, G. E., Osindero, S., and Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18 (7), 1527-1554.
  • Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 28 (5), 1-26.
  • Kuhn, M. and Johnson, K. (2013). Applied Predictive Modeling. New York: Springer.
  • Lichman, M. (2013). UCI Machine Learning Repository (http://archive.ics.uci.edu/ml). Irvine, CA: University of California, School of Information and Computer Science.
  • Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT press.
  • Nair, V., and Hinton, G. E. (2010). Rectified Linear Units Improve Restricted Boltzmann Machines. In Proceedings of the 27th International Conference on Machine Learning (ICML-10) (pp. 807-814).
  • Riedmiller, M., and Braun, H. (1993). A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm. In Neural Networks, 1993, IEEE International Conference.
  • Schmidhuber, J. (2015). Deep Learning in Neural Networks: An Overview. Neural Networks, 61, 85-117.
  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. The Journal of Machine Learning Research, 15(1), 1929-1958.
  • Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S-Plus. Fourth Edition. Springer.
  • Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P. A. (2008, July). Extracting and Composing Robust Features with Denoising Autoencoders. In Proceedings of the 25th International Conference on Machine Learning (pp. 1096-1103). ACM.
  • Zeiler, M. D. (2012). ADADELTA: An Adaptive Learning Rate Method. arXiv preprint arXiv:1212.5701.
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