where is a matrix of weight vectors with columns, is a vector of class biases, and is a probability normalization term.
By varying the location and vector of class III mechanics, orthodontists can differentially alter the magnitude of forward, downward, and rotational movement of the maxilla.
When the output layer has a softmax activation function, these labels will be a vector of class conditional probabilities summing to 1.
where T l n j denotes the j th element of the target vector of class l n, and s j is the j th element of a single synthesized output vector belonging to class l n.
where N k is the number of training samples in the class k and (mathbf {m}_{k} = frac {1}{N_{k}}sum _{i=1}^{N_{k}}mathbf {h}left (mathbf {x}_{i}right)) is the mean vector of class k.
In contrast to earlier works, they used the averaged vector of class probability output by the ensemble to relabel training data sampled from the original training data set, rather than the single class having the maximum probability.
By varying the location of N2 mini-implants and vector of class III mechanics, clinicians can differentially alter the magnitude of forward, downward, and rotational movement of the maxilla.
Hinton and Vinyals et al. [16] also refers to the vector of class conditional probabilities as soft targets and adds a "temperature" variable to the softmax function in the output layer in order to produce a softer output distribution over the classes, as an alternative to using the logits as targets.
We also define as the (1 × n) target vector of class labels for samples.
Equivalently, MCC can be defined as the Pearson's correlation coefficient of the binary vector of class labels compared to the binary vector of predicted class labels.
The adjacency matrix representation permits easy calculation of the vector of class probabilities, given knowledge of which evidence variables to use.
