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
Artificial NeuroGlial Networks, Encyclopedia of Artificial Intelligence, pp.167-171, 2009. ,
Minimum Message Length Inference: Theory and Applications, 1996. ,
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
Algorithmic Probability Solve the Problem of Induction, Oxbridge Research, P.O.B, vol.391887, 1997. ,
Modeling by shortest data description, Automatica, vol.14, issue.5, pp.465-471, 1978. ,
DOI : 10.1016/0005-1098(78)90005-5
An Information Measure for Classification, The Computer Journal, vol.11, issue.2, pp.185-195, 1968. ,
DOI : 10.1093/comjnl/11.2.185
Ideal MDL and Its Relation to Bayesianism Proceeding of ISIS: Information, Statistics and Induction in Science, pp.282-291, 1996. ,
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
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
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
A Formal Theory of Inductive Inference, parts 1-2. Information and Control, pp.1-22, 1964. ,
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
Non-)Equivalence of Universal Priors. Solomonoff 85 th Memorial Conference. abs/1111, p.3854, 2011. ,
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
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
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
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
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
Multilayer feedforward networks are universal approximators, Multilayer Feedforward Networks are Universal Approximators, pp.359-366, 1989. ,
DOI : 10.1016/0893-6080(89)90020-8
New Paradigm of Learnable Computer Vision Algorithms Based on the Representational MDL Principle. Proceeding of SPIE, p.769606, 2010. ,
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