Extended Hamiltonian Learning on Riemannian Manifolds: Numerical Aspects

@article{Fiori2012ExtendedHL,
  title={Extended Hamiltonian Learning on Riemannian Manifolds: Numerical Aspects},
  author={Simone G. O. Fiori},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2012},
  volume={23},
  pages={7-21}
}
This paper is the second part of a study initiated with the paper S. Fiori, “Extended Hamiltonian learning on Riemannian manifolds: Theoretical aspects,” IEEE Trans. Neural Netw., vol. 22, no. 5, pp. 687-700, May 2011, which aimed at introducing a general framework to develop a theory of learning on differentiable manifolds by extended Hamiltonian stationary-action principle. This paper discusses the numerical implementation of the extended Hamiltonian learning paradigm by making use of notions… CONTINUE READING
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