1. Andrews, S., Tsochantaridis, I., & Hofmann, T. (2003). Support vector machines for multiple-instance learning. In Advances in neural information processing systems (pp. 561–568).
2. Ascher, D., Dubois, P., Hinsen, K., Hugunin, J., & Oliphant, T. (2001). Numerical Python. Livermore: Lawrence Livermore National Laboratory.
3. Auer, P., Long, P., & Srinivasan, A. (1997). Approximating hyper-rectangles: learning and pseudo-random sets. In Proceedings of the 29th annual ACM symposium on the theory of computation (pp. 314–323). New York: ACM.
4. MPS-SIAM series on optimization;A. Ben-Tal,2001
5. Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281–305.