Sentence examples for sparse in terms of from inspiring English sources

The resulting representation is sparse in terms of adjustable parameters, thereby lending itself easily to automatic analysis and classification, yet it is readily intelligible for the expert.

Ferguson added: "We have been a bit sparse in terms of goals scored in the last few weeks but we won 4-1 laSaturdaydandand a 2-0 todandand I think everyone would say we should have scored more.

As we can see in the long quote from TF 170 and other passages in The Incoherence (e.g. TF 1), the operative conception of entailment for al-Ghazālī appears to that of "conceptual" entailment, where the concepts of "fire" and "cotton" are rather sparse in terms of content.

When representations become sparse, in terms of activity profiles and density of responsive neuronal populations, computational structures evolve into simplified curved manifolds capable of greater efficiency and specificity within the framework of larger "over-complete" representations, where the dimensionality of the computational space exceeds input dimensionality.

The more you put in, the better your drawing will look, since campsites are rarely sparse in terms of the landscape.

The Colorado River region is the sparsest in terms of the number of HUCs with groundwater dependent wetlands, where only 49% of the HUC units in that region contained groundwater dependent wetlands (Figure 6).

The accuracy and efficiency are compared to the well-established least angle regression (LAR) sparse PCE, and results show that the developed method is superior to the LAR-based sparse PCE in terms of efficiency and accuracy.

Here, it is assumed that all the image patches have sparse representation in terms of a dictionary.

Due to the limited writing space, we mainly review the tracking works related to sparse representation in terms of performance and complexity.

Due to the label information being considered, the proposed method will be more powerful than the typical methods, such as SIFT based bag-of-feature and sparse coding, in terms of discovering the latent semantic information.

The reconstructed images are obtained from the eigenvectors for the 2D PCA-, 2D LDA-, and 2D CCA-based methods, the 2D GHMs, and the proposed sparse features in terms of 2D DGHMs.