Sentence examples for much higher dimension from inspiring English sources

The motivation for mapping instances rather than features is because when performing text classification tasks, the application in the study, the feature spaces are often of much higher dimension than the number of instances.

This is noteworthy because we here use the RMSD between all 436 non-hydrogen atoms corresponding to a much higher dimension of the conformation space.

To tackle this issue, we developed a version of the SEICWH model that allows us to track both the infection and the immunological histories of individuals and used this model to compute F I., F II. and F IS. Since this model is of much higher dimension than that of Fig. 1B, we refer to Text S4 for a more detailed description.

When a user has an object classifier trained on images of size M × N and an input image of much higher dimensions, it is possible to detect all instances of the object of interest in the image.

In classification experiments, the first three formant frequencies were found to provide comparable or improved performance relative to the Mel frequency cepstral coefficients, which have a much higher feature dimension.

The dimensional constraints on the designed device put forward much higher request on the dimension synthesis of DELTA mechanism to satisfy the requirements of output force and cube workspace simultaneously.

Here, however, we want to stress that it is this peculiar feature of divergencies itself which make the analysis of graphs associated with manifolds of higher dimension much more complicated.

Similarly, funny punch lines were rated much higher on the "surprise" dimension than the congruous endings, F 1, 6) = 229.1, p <.0001, but in this respect differed only marginally from the incongruous endings, F 1, 6) = 5.7, p <.1.

The problem is that a majority of the data sets in the biomedical domain are weakly structured and non-standardized [13], and most data are in dimensions much higher than 3, and despite human experts are excellent in pattern recognition for dimensions (le 3), such data make manual analysis often impossible.

This is commonplace for sets represented in very high-dimensional spaces and a direct consequence of data under-sampling, as the dimension of the data points (number of recorded neurons) is much higher than the sample training data dimension (trial numbers).

But in our case SSA could not be applied since the number of dimensions was much higher than the number of trials.