Prediction Kernel

[seveibar]

In prediction kernel mode, a prediction kernel model learns confidence of belief answers from the differences of latent beliefs of sources. e.g. if there are three latent beliefs, each will have an answer and those answers will have differences. So the training data is

X=[d(b1_answer, b2_answer), d(b2_answer, b3_answer), d(b1_answer, b3_answer)]

Where b_i is a belief. The answer for each belief can be selected from the best representative of each belief (naive, but probably the theoretical best unless answer interpolation is implemented)

The ground truth/prediction is

y=[conf_b1, conf_b2, conf_b3]

Any model can be used to run the prediction. The model used would be called the "prediction kernel".