Sentence examples for prior of class 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.

where g(i) indicates the class label of x i and P j) denotes the prior probability of class j.

where P C k = N k N tot is a prior probability of class membership (N k – the number of compounds belonging to class C k ; N tot – the total number of compounds), whereas p t), the marginal probability density function, is the normalization factor: p t = ∑ k p t C k · P C k (4).

where p z x | X i ) is the likelihood function, P(X i ) is the prior probability of class X i, and P (z x ) is the evidence, computed as P ( z x ) = ∑ i ∈ S p ( z x | X i ) P ( X i ), which is a scale factor that ensures that the posterior probabilities sum to one.

In a sense, this is be expected, since the SVM and ANN utilise our prior knowledge of class membership to find the optimal linear mapping for classifying the data.

The reason for the increased sensitivity of our ANN here is its utilisation of our prior knowledge of class membership and its efficiency in exploring the space of all possible linear (and non-linear) mappings and identifying the choice that maximises the classifier's sensitivity automatically.

Let τ(k) be the prior probability of class k.

Let t be a test sample, the class label of t is determined by: (7) and where π k is the prior probability of class k.

The Bhattacharyya statistical distance, J ij, was used to measure the separation between two diagnostic classes (i,j) [ 48, 49] and was generalized to all spectral parameters (n = 5) by: J = ∑ i = 1 5 ∑ j = 1 5 P i P j J ij Here, Pi represents the prior probability of class i determined by its fraction of pixels in the training set.

The algorithm is shown in Figure 4 and updating W i can use Equation 1: (1) W i = W i − ∑ k = 1 K D H n ⋅ K + ∑ c = 1 C − 1 P c ⋅ ∑ k = 1 K D M c n ⋅ K where n c is the number of instances in class c, D H (or D M c ) is the sum of distance between the selected instance and each H (or M c ), P c is the prior probability of class c.

Upon entering class, students respond to the following prompt in their journals (written on the board prior to class): "Which of these items was the product of design?