[1] Sch¨olkopf B., Smola A.J., Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, London, England, 2002.

[2] Steinwart I., On the influence of the kernel on the consistency of support vector machines. The Journal of Machine Learning Research, 2002, 2, pp. 67–93.

[3] Steinwart I., Consistency of support vector machines and other regularized kernel classifiers. IEEE Transactions on Information Theory, 2005, 51(1), pp. 128–142.

[4] Czarnecki W.M., Tabor J., Multithreshold entropy linear classifier. arXiv preprint arXiv:1408.1054, 2014.

[5] Jenssen R., Principe J.C., Erdogmus D., Eltoft T., The cauchy–schwarz divergence and parzen windowing: Connections to graph theory and mercer kernels. Journal of the Franklin Institute, 2006, 343(6), pp. 614–629.

[6] Silverman B.W., Density estimation for statistics and data analysis. vol. 26. CRC press, Boca Raton 1986.

[7] Principe J.C., Information theoretic learning: R´enyi’s entropy and kernel perspectives. Springer, Gainesville 2010.

[8] Vapnik V., The nature of statistical learning theory. Springer, New York 2000.

[9] Ho T.K., Kleinberg E.M., Building projectable classifiers of arbitrary complexity. In: Proceedings of the 13th International Conference on Pattern Recognition, 1996. vol. 2., IEEE, 1996, pp. 880–885.

[10] LeCun Y., Cortes C., The mnist database of handwritten digits, 1998, http://yann.lecun.com/exdb/mnist/.