M. Khashei and M. Bijari, An artificial neural network (p,d,q) model for timeseries forecasting, Expert Systems with Applications, vol.37, issue.1, pp.479-489, 2010.
DOI : 10.1016/j.eswa.2009.05.044

A. B. Pazos, A. A. Gonzalez, and F. M. Pazos, Artificial NeuroGlial Networks, Encyclopedia of Artificial Intelligence, pp.167-171, 2009.

R. A. Baxter, Minimum Message Length Inference: Theory and Applications, 1996.

A. N. Kolmogorov, Logical basis for information theory and probability theory, IEEE Transactions on Information Theory, vol.14, issue.5, pp.662-664, 1968.
DOI : 10.1109/TIT.1968.1054210

R. Solomonoff, Algorithmic Probability Solve the Problem of Induction, Oxbridge Research, P.O.B, vol.391887, 1997.

J. J. Rissanen, Modeling by shortest data description, Automatica, vol.14, issue.5, pp.465-471, 1978.
DOI : 10.1016/0005-1098(78)90005-5

C. S. Wallace and D. M. Boulton, An Information Measure for Classification, The Computer Journal, vol.11, issue.2, pp.185-195, 1968.
DOI : 10.1093/comjnl/11.2.185

P. Vitanyi and M. Li, Ideal MDL and Its Relation to Bayesianism Proceeding of ISIS: Information, Statistics and Induction in Science, pp.282-291, 1996.

Y. Zhao and M. Small, Minimum description length criterion for modeling of chaotic attractors with multilayer perceptron networks, IEEE Transactions on Circuits and Systems I: Regular Papers, vol.53, issue.3, pp.722-732, 2006.
DOI : 10.1109/TCSI.2005.858321

A. S. Potapov, Comparative analysis of structural representations of images, based on the principle of representational minimum description length, Journal of Optical Technology, vol.75, issue.11, pp.715-720, 2008.
DOI : 10.1364/JOT.75.000715

M. Li and P. Vitanyi, Philosophical Issues in Kolmogorov Complexity (Invited Lecture), Proc. on Automata, Languages and Programming, pp.1-15, 1992.
DOI : 10.1007/3-540-55719-9_59

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.3558

R. Solomonoff, A Formal Theory of Inductive Inference, parts 1-2. Information and Control, pp.1-22, 1964.

P. Vitanyi and M. Li, Minimum description length induction, Bayesianism, and Kolmogorov complexity, IEEE Transactions on Information Theory, vol.46, issue.2, pp.446-464, 2000.
DOI : 10.1109/18.825807

URL : http://arxiv.org/abs/cs/9901014

I. Wood, P. Sunehag, and M. Hutter, Non-)Equivalence of Universal Priors. Solomonoff 85 th Memorial Conference. abs/1111, p.3854, 2011.

H. Lappalainen, Using an MDL-based cost function with neural networks, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227), pp.2384-2389, 1998.
DOI : 10.1109/IJCNN.1998.687235

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.163

J. Wang and Y. Hsu, An MDL-based Hammerstein recurrent neural network for control applications, Neurocomputing, vol.74, issue.1-3, pp.315-327, 2010.
DOI : 10.1016/j.neucom.2010.03.011

Y. I. Molkov, D. N. Mukhin, E. M. Loskutov, A. M. Feigin, and G. A. Fidelin, Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series, Physical Review E, vol.80, issue.4, pp.1-6, 2009.
DOI : 10.1103/PhysRevE.80.046207

M. Small and C. K. Tse, Minimum description length neural networks for time series prediction, Physical Review E, vol.66, issue.6, pp.1-12, 2002.
DOI : 10.1103/PhysRevE.66.066701

URL : http://ira.lib.polyu.edu.hk/bitstream/10397/711/1/series-prediction_02.pdf

A. Leonardis and H. Bischof, An efficient MDL-based construction of RBF networks, Neural Networks, vol.11, issue.5, pp.963-973, 1998.
DOI : 10.1016/S0893-6080(98)00051-3

K. Hornik, M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators, Multilayer Feedforward Networks are Universal Approximators, pp.359-366, 1989.
DOI : 10.1016/0893-6080(89)90020-8

A. S. Potapov, I. A. Malyshev, A. E. Puysha, and A. N. Averkin, New Paradigm of Learnable Computer Vision Algorithms Based on the Representational MDL Principle. Proceeding of SPIE, p.769606, 2010.

J. Schmidhuber, The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions, Proceedings of the 15 th Annual Conference on Computational Learning Theory. Sydney. Australia. LNAI, pp.216-228, 2002.
DOI : 10.1007/3-540-45435-7_15

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.3817