Sentence examples for of possible classes from inspiring English sources
where is the set of possible classes (modulation schemes), is the prior of class, is a data point of class, is the mean of class and represents the mean of all classes.
The GSI was originally introduced to measure the separability of data in a binary classification problem, i.e., where the number of possible classes is two, while here we are dealing with a number of classes equal to the number of users in the dataset.
Multi-dimensional categorization is concerned with categorizing items using multiple sets of classes, where for each dimension a different set of possible classes is defined.
For example, the particular classes students are enrolled in could be considered a random effect if the subset of classes used in a study can be seen as having been chosen at random from a larger pool of possible classes.
A classification problem is the following [ 33]: given a set of training instances, each described with a set of n attributes and each belonging to exactly one of a certain number of possible classes, learn to classify new, unseen objects.
According to Bayes' Theorem, the conditional probability that s/he belongs to competence level L i given the data vector Z is thus: (1) where 'k' is the number of possible classes.
There are seven Djinn of each element, and these Djinn can be mixed and matched to the four characters, allowing a large array of possible class setups and a variety of combat options.
There is a total of 72 Djinn encompassing the four elements which can be arranged in almost any evenly distributed manner to the eventual eight characters, making for a large array of possible class setups for all eight playable characters, allowing a variety of combat options.
A Cost Curve analysis is a graphical technique used to visualize the performance (error rate or expected cost) of binary classifiers over the full range of possible class distributions and misclassification costs.
This is due to the fact, that p(M; N, n, m) decreases if the number of classes and thus the number of possible class distributions increases.
We specify the column dimension of the new representation V = [ v j k ] ∈ ℝ N × K of the original data with respect to the new basis as the same as the number of possible class labels q, i.e., we set K equals to q, each dimension of the new representation corresponds to one class label.
