Sentence examples for 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.

In our experiments we choose naïve bayes classier (NBC) and nearest means scale classifier (NMSC) [29] for supervised learning, NBC and NMSC-based RFA feature selection methods are denoted as NBC-MSC and NMSC-MSC, respectively.

One of the simplest classification methods is nearest mean classifier (NMC) that is a parametric, unbiased, and probabilistic method.

The obtained vectors were classified by NN (Nearest Neighbour classifier), NM (Nearest Mean classifier) and GMM (Gaussian Mixture Models).

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.

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].

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.

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.

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].