Sentence examples for multidimensional feature vector from inspiring English sources

Instead, multiple variances are determined for different frequency ranges resulting in a multidimensional feature vector.

Using modern Machine Learning techniques, we present a supervised classification method based on a Random Forest classifier, using a multidimensional feature vector of collaboration network centrality metrics.

Before classification, a multidimensional feature vector is computed for each pixel, such that in the corresponding feature space, vessels and background are more separable than in the original image space.

Hence each article is represented by a multidimensional feature vector where each dimension corresponds to a term (feature) within the literature collection.

In our study, an image was first represented by a multidimensional feature vector, which was then fed into each semantic model to calculate its relevance scores.

In a nutshell, the preprocessing methodology SFX is a way of transforming a two-dimensional time-series (amplitude versus time) into a multidimensional feature vector that has all the essential attributes sufficient to characterize the original time-series voice data.

To detect speech based on the spectral shape, the multidimensional feature vectors are typically modeled using codebooks.

Compared with prior studies, we not only focused on features extraction but also extended our work to four aspects: accurate pretreatment of the benchmark data, extraction of multidimensional feature vectors [ 18], rebuilding training sets through the oversampling and undersampling approaches, and adoption of a novel ensemble classifier.

Once a shape descriptor has been adopted, the comparison of mode shapes is transformed to a comparison of multidimensional shape feature vectors.

The training samples are audio feature vectors which are distributed in a multidimensional feature space.

Linear discriminant functions define decision hyperplanes in a multidimensional feature space: g(x ) = w T · x + w0 where w is the weight vector to be optimized that is orthogonal to the decision hyperplane and w0 is the threshold.