Sentence examples for three dimensional feature space from inspiring English sources

The method is based on analysis of the distribution of all genes on a microarray mapped to a three dimensional feature space composed of average difference of gene expression (logarithm of fold change), total variance and average expression level.

The method is based on an analysis of the intensity distribution of all microarray probe sets mapped to a three dimensional feature space composed of average expression level, average difference of gene expression and total variance.

This paper presents the description of a method, called the distributional fold change (DFC) test, which is based on the analysis of the distribution of intensities of all features on a microarray mapped to a three dimensional feature space composed of the average difference of gene expression (logarithm of fold change), total variance and average expression level.

For analysis of the SUA, spike sorting was performed offline using the Offline Spike Sorter (Plexon, Inc., Dallas TX), primarily through manual isolation of spike clusters in two or three dimensional feature spaces.

We projected the strains in the six dimensional feature space onto two dimensions for each ΔT.

Here we have considered two data sets from UCI machine learning database having nine and ten dimensional feature spaces for classification.

Three feature extraction methods are comparatively examined in reduced dimensional feature space.

A kernel can be seen as an inner product (an operation on two vectors that produces a scalar) in a high/infinite dimensional feature space.

The two core concepts of SVM algorithm are mapping input vectors into a high dimensional feature space and structural risk minimization.

If the surface separating the two classes is not linear, the data points can be transformed to another high dimensional feature space where the problem is linearly separable.

The separating hyperplane is determined by a) mapping the input space into a higher dimensional feature space through a kernel function, and b) constructing in this feature space two maximal margin hyperplanes [ 16] to separate the mapped data samples in the higher dimensional space.