Stationary Fokker – Planck Learning for the Optimization of Parameters in Nonlinear Models

Author:

Peña Dexmont,Sánchez Ricardo,Berrones Arturo

Publisher

Springer Berlin Heidelberg

Reference16 articles.

1. Lecture Notes in Computer Science;A. Berrones,2007

2. Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984)

3. Grasman, J., van Herwaarden, O.A.: Asymptotic Methods for the Fokker–Planck Equation and the Exit Problem in Applications. Springer, Heidelberg (1999)

4. Haykin, S.: Neural Networks: a Comprehensive Foundation. Prentice Hall, New Jersey (1999)

5. Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (2004)

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Ground states of 2D ±JIsing spin glasses obtained via stationary Fokker–Planck sampling;Journal of Statistical Mechanics: Theory and Experiment;2008-10-28

2. Learning Probability Densities of Optimization Problems with Constraints and Uncertainty;MICAI 2008: Advances in Artificial Intelligence;2008

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