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The problem of selecting the best subset of hypotheses still remains and the proposed iterative model selection scheme can easily be adapted by substitution of the homography model for images with a rigid transformation model for the point clouds.
Next, we construct a model selection scheme based on the White's Neural Network test for non-linearity or alternatively combine FARIMA and Neural Networks into hybrid forecasting models.
DCM is a platform for inferring the architecture of dynamical systems, combining a user-dependent model specification step with a Bayesian model selection scheme.
ProtTest [21] is a model selection scheme for protein sequences based on score files generated by PhyML.
a) We have revised the model selection scheme.
Our iterative model selection scheme perfectly supports this.
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Based on these properties, a first model order selection scheme can be proposed (29).
Besides the global eigenvalues-based schemes, we also propose a tensor data-based multi-dimensional model order selection scheme.
Followed by the closed-form PARAFAC-based model order selection scheme is proposed for white and also colored noise scenarios.
However, the model order selection scheme in (29) yields a Probability of correct Detection (PoD) inferior to the some MOS techniques found in the literature.
Therefore, as a first important contribution, we have proposed in [1, 2] the one-dimensional model order selection scheme called Modified Exponential Fitting Test (M-EFT), which outperforms all the other schemes for scenarios involving white Gaussian noise.
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model modification scheme
model correction scheme
model representation scheme
model description scheme
model update scheme
model interaction scheme
model validation scheme
model selection customer
model control scheme
model selection strategy
model recombination scheme
model selection step
model estimation scheme
model selection accuracy
model selection bias
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