A fundamental question in learning to classify 3D shapes is how to treat the data in a way that would allow us to construct efficient and accurate geometric processing and analysis procedures. Here, we restrict ourselves to networks that operate on point clouds. There were several attempts to tr...
(a) Amino acid composition (b) Amino acid sequence (c) Helical structure in proteins (c) The bonding of the protein to nonprotein components (e) The three dimensional structure A dipeptide has two structural isomers. For example, combinin...
By using an appropriate set of photometric filters, e.g., the automated two-dimensional classification from multicolor photometry in the Vilnius system [38,39,40,41,42], it would be possible to make a stellar classification with a set of reddening-free Q parameters. Figure 2. Free-reddening...
By using an appropriate set of photometric filters, e.g., the automated two-dimensional classification from multicolor photometry in the Vilnius system [38,39,40,41,42], it would be possible to make a stellar classification with a set of reddening-free Q parameters. Figure 2. Free-reddening...
An x-dimensional feature vector is computed across the resulting histograms. The resulting vector is the one that will be used as a feature vector for the classification process, but we can also see how the magnitudes and angles change depending on the parameters, Figure 8 shows an example of...
This is not the case for the other locations, as samples are more scattered in the two-dimensional space, with many of them exhibiting high values for the molecular marker (X-axis) and low values for the morphological marker (Y-axis) or, less commonly, the opposite. For example, in the...
two-dimensional greyscale images without principal component analysis (PCA) or downscaling. We compared the performance of two-dimensional CNNs with the deep cross neural network (DCN), support vector machine, random forest, gradient boosting machine, and decision tree in individual tree species ...
Classifier—1D CNN: EEG time series has a two-dimensional form, channel × time, so it has an unbalanced form in that the number of time samples is likely to be far larger than the number of channels. When a commonly used square-shaped 2D convolution filter is applied, the channel's ...
Table 9. Two-phase loss coefficient selection. Table 10 shows that the two-phase model had slightly higher accuracy (0.019) on two-fold cross-validation averaged over input image shapes of (192, 192) and (128, 128). Table 10. Two-fold accuracy for two phases or classification phase only...