Hierarchical clustering also splits the cells broadly into two classes, labelled Class I and Class II in Fig. 5A.
The final model predicts the class labels.
It considers a linear relationship between data and class labels.
The leaf nodes, on the other hand, represent class labels.
C is the set of class labels to learn.
The first hypothesis is that the predicted class labels are statistically independent from the true class labels.
The second hypothesis is that the predicted class labels are statistically dependent on the true class labels.
In multiple-instance learning (MIL), class labels are attached to bags instead of instances, and the goal is to predict the class labels of unseen bags.
Table 1a, b contains a snapshot from a classification example in which the class label of a sample document is predicted in each of the following two cases: (a) 3 class labels, and (b) 6 class labels (Table 1a, b).
These measures are based on computing the performance metric of interest for each target class label, and then averaging them for the (N) class labels.
Image features are derived from pixel information, which is not semantically related to class labels, as opposed to word features that have semantic interpretability to class labels.
