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Calculate the sum of squared deviations from the class means (SDCM). Choose a new way of dividing the data into classes, perhaps by moving one or more data points from one class to a different one. New class deviations are then calculated, and the process is repeated until the sum of the within class deviations reaches a minimal value. [1] [5]
Otsu's method is related to Fisher's linear discriminant, and was created to binarize the histogram of pixels in a grayscale image by optimally picking the black/white threshold that minimizes intra-class variance and maximizes inter-class variance within/between grayscales assigned to black and white pixel classes.
H 1 does not separate the classes. H 2 does, but only with a small margin. H 3 separates them with the maximum margin.. In machine learning, the margin of a single data point is defined to be the distance from the data point to a decision boundary.
Decision boundaries are not always clear cut. That is, the transition from one class in the feature space to another is not discontinuous, but gradual. This effect is common in fuzzy logic based classification algorithms, where membership in one class or another is ambiguous. Decision boundaries can be approximations of optimal stopping boundaries.
In statistics, the intraclass correlation, or the intraclass correlation coefficient (ICC), [1] is a descriptive statistic that can be used when quantitative measurements are made on units that are organized into groups. It describes how strongly units in the same group resemble each other.
In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method. It was first developed by Evelyn Fix and Joseph Hodges in 1951, [1] and later expanded by Thomas Cover. [2] Most often, it is used for classification, as a k-NN classifier, the output of which is a class membership
Suppose a pair (,) takes values in {,, …,}, where is the class label of an element whose features are given by .Assume that the conditional distribution of X, given that the label Y takes the value r is given by (=) =,, …, where "" means "is distributed as", and where denotes a probability distribution.
A classification model (classifier or diagnosis [7]) is a mapping of instances between certain classes/groups.Because the classifier or diagnosis result can be an arbitrary real value (continuous output), the classifier boundary between classes must be determined by a threshold value (for instance, to determine whether a person has hypertension based on a blood pressure measure).