Sentence examples for the nearest means from inspiring English sources

In order to estimate the canonical projectors, we define the nearest rank-1 matrix approximation of K xy by: boldsymbol{K}_{1} = d_{1},boldsymbol{u}_{1}boldsymbol{v}_{1}^{T}~, where the nearest means that the squared Frobenius norm between K xy and K 1, defined by (big Vert boldsymbol {K}_{xy}-boldsymbol {K}_{1} big Vert _{F}^{2}), is minimal.

The proposed synchronous BCI design was tested on 16 subjects in offline and online experimental tasks using support vector machines, linear discriminant analysis and the nearest mean classifier.

b k-means is a method of clustering that aims to partition observations into K clusters in which each observation belongs to the cluster with the nearest mean.

K-means clustering aims to partition the input observations into different clusters in which each observation belongs to the cluster with the nearest mean, and the center of each cluster is taken as the average capillary pressure curve.

A series of K-means (K-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean) which derived grouping and coordinator selection algorithms are proposed in [56].

The k-means clustering is one of the most commonly used method for finding K clusters or codebook in the N observations and each observation belongs to the corresponding cluster with the nearest mean [23].

We use the k-means algorithm to partition the n sensor nodes into k clusters in which each sensor node belongs to the cluster with the nearest mean of point.

For given K central points (the mean points of the cluster samples), each sample is allocated in a cluster represented by the nearest mean point of its Euclidean distance, and the samples are divided into K clusters.

The k-means clustering method is a widely used unsupervised pattern recognition algorithm that aims to partition n observations into k clusters, where each observation belongs to the cluster with the nearest mean [31, 32, 33, 34].

Simple k-means clustering is a method of cluster analysis which aims to partition n objects into k clusters in which each object belongs to the cluster with the nearest mean.

Classification is undertaken according to the nearest mean pattern.