Sentence examples for map the feature from inspiring English sources

The framework is designed to compute subspace feature of arbitrary face image, then map the feature to its counterpart in another subspace learned with 3D faces, and reconstruct the 3D face using the counterpart feature.

In this work, we experiment with Fisher vector encoding to map the feature vectors into a higher dimensional space and use linear support vector machine (SVM) for improving the performance of inter turn fault diagnosis in a 3 kVA synchronous generator.

Kernel functions help to map the feature space into higher dimensional space.

This can be explained by the lack of a hydrogen bond accepting group in compound 2 that could map the feature towards His447, near position 17.

In the case of non-linearly separable classification problems, SVM uses kernel functions in order to implicitly map the feature vectors to a very high-dimensional feature space to obtain non-linear boundaries.

In brief, the algorithm uses the information about each feature vector's nearest neighbours that is, those vectors with the smallest euclidean distance to a given vector to map the feature vectors, which are originally points in R n (a space in n dimensions) as points in R (a plane), so that the proximity in R reflects the proximity in R n.

We can map the features to the reproducing kernel hilbert space (RKHS) with a kernel function k defined over each of the two feature types.

These observations begin to map the features of 160 which contribute to activity against the NS2 autoprotease.

This LSA transformation mapped the feature dimensions to the axes with large variations in the reduced LSA space.

Then, the 2000 RoIs are mapped the feature vector according to the bounding box of those RoIs, and do not extract its features.

On the other hand, feature extraction techniques seek to reduce the dimensionality of the data by mapping the feature space onto a new lower-dimensional space.