Full-information maximum likelihood was used to estimate probabilities of class membership for individuals who responded to at least one of the smoking questionnaires, but were missing data from at least one questionnaire (4284/7322 or 59% of sample) 34.
By statistical recognition is meant the feature vector y extracted from test HRRP sample x will be assigned to the class with maximum posterior probability p(c|y), where c ∈ {1,..., C} denotes the class membership.
With LCA, the probabilities of class membership as well as the probabilities of item response (conditional on class membership) are estimated based on maximum likelihood estimation (Lanza et al. 2007).
Class membership is therefore not a transitive relation.
The relation of class inclusion, however (to be carefully distinguished from class membership), is transitive.
The algorithm output is a class membership.
Maximum class size is 12 people.
As expected, training conditions and student domain predicted class membership.
Various criteria on boundedness, compactness and Hilbert Schmidt class membership are established.
The results from FCM clustering algorithm belong to class membership.
