Sentence examples for dimensions of feature from inspiring English sources

The number of dimensions of feature vector h t was 400[Gauss] × 40[dim/(Gauss · frame)] × 9[frame].

Thus, we defined several NN configurations as follows:  Single NN configuration, where it is assumed that the transformation on each dimension is the same as each other, so one NN is used to transform all dimensions of feature.

The mapping of individual structural paths to dimensions of feature vectors is defined before feature extraction and during feature selection and is applied uniformly to all structural maps prior to both training and classification.

This is because the multi-feature fusion location algorithm using multi-feature extraction acceleration and different dimensions of feature fusion estimation moving trajectory of the target object not only reduces the processing time but also the computational complexity and the positioning method, so that the positioning acceleration effect is very obvious.

These predicted features not only increased the complexity of the algorithm, but also increased the dimensions of feature vectors.

For developing the CLCLpred model, 3 window sizes are necessary; the masking-window size which is used to calculate average local conservation scores, the outside sliding-window size which would finally decide the dimensions of feature vectors, and the inside smoothing-window size which is used to strengthen the local conservation features.

The dimensions of features on the board and the thickness of the solder-mask polymer also influence skipping.

The higher one, where the dimensions of features are measured in centimetres, has many techniques at its disposal, notably functional magnetic-resonance imaging (fMRI), which measures changes in tissues' fuel consumption.

AFM is ideally suited for both the visualization of nanostructure materials and the measurement of the spatial dimensions of features on the nanomaterial surfaces.

Shi et al. employed the discrete wavelet transformation and summary statistics to reduce the dimensions of features.

However, in this paper, we would present a method that can produce comparable prediction performance with only about 100 dimensions of features and without using any dimensional reduction algorithm.