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where Z is the set of sparse representations of the training set X and the l 1-norm ||Z||1 is used for enforcing sparsity.
Here mean squared error is calculated from HR to LR and then LR to HR sparse representations of the training patch pairs after updating the HR and LR mapping matrices in each iteration.
In this space, the representations of the training instances are identity matrix.
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Early mapping studies in adult neocortex suggested that the representations of the trained digits in somatosensory cortex (Recanzone et al. 1992) or trained tones in auditory cortex (Recanzone et al. 1993) were expanded.
The optimal graph improves the representation of the training set and provides the best configuration of components according to their purities.
We propose to apply the diffusion framework to estimate an HMM model, by computing a reduced dimensionality representation of the training set and estimating the meta-states.
The definition of the domain of applicability of the training set using the vectorial representation of the training samples by molecular descriptors prevents a direct application to models trained by structured kernel-based machine learning approaches.
Experiments demonstrate that this results in a more effective representation of the training vectors by the codevectors and that the final codebook is nearer to the optimal solution in applications such as image compression.
In this step, the parameters of the HDLA model are learned from the training images, and it proceeds in three stages: (i) visual dictionary learning (ii) generating Bag of Visual Word (BoVW) representation of the training images and (iii) layer-by-layer training of the HDLA model in an unsupervised fashion.
The starting point for both classification methods is a vector representation of the training data.
Significantly, it provides a good visual representation of the trained LS-SVMlab.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com