Sentence examples for the class variance from inspiring English sources

The Otsu method selects an optimal threshold by maximizing the class variance and it works well when the entire distribution is segmented into two classes.

The intensity value, d, that produces the maximum sum of the class variance is chosen as the threshold value T. Similarly, it can extend to MTS of an image in order to find and detect the object (brain tumor) which is localized in one of these levels of image intensity.

Compared to Caltech 101, Caltech 256 is more challenging because the objects are not centered in the images and the intra class variance is much higher.

Fisher [ 4] defined LDA as a transformation that maximises the between class variance relative to the within class variance.

Second, the intra class variance (dissimilates between images in the same class) and inter class relationship (similarities between images from different classes) are large.

These assumptions were satisfied for all platforms except for F tests conducted for the Affymetrix data (here, we used the within class variance calculated for each target).

In particular, in Caltech 256 the intra class variance is very large, making it more challenging for image classification.

Using an analogous definition for the variance in the noncoincident class S n, the total within class variance is given by eq 3. 3 The particular unit vector that maximizes the Fisher linear discriminant for a two-class system is generated from S w –1: 4The vector directly serves as the filter function F to be used for digital deconvolution of the raw data.

Multiple threshold level values are determined by exhaustively searching through all sets of threshold levels for the given number of classes (K) in to which the image needs to be divided to find the combination that gives the minimum within class variance (or maximum inter-class variance).

Based on this idea, in this work, we propose the regularized minimum class variance extreme learning machine (RMCVELM) which minimizes the empirical risk, structural risk, and intra-class variance of training data in the decision space simultaneously.

Based on these matrices, we computed a class difference matrix and a squared Mahalanobis class distance matrix, that is, the squared difference between the two class element values, divided by the average of the two class variances for that particular GLEM element (Nielsen and Danielsen, 2006).