Patent ID: 11907809
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 14:
15. A computer program product for training a prediction model for predicting a label to be allocated to a prediction target explanatory variable set, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:
acquiring a set of training data comprising a set of explanatory variables and a label allocated to the set of explanatory variables;
extracting a plurality of explanatory variable subsets from the set of explanatory variables, wherein respective ones of the plurality of explanatory variable subsets are allocated a different weight coefficient;
generating a feature vector, concerning each of the plurality of explanatory variable subsets, the feature vector comprising a plurality of feature values;
executing Bayesian inference using prior distributions of a regression vector comprising a plurality of regression coefficients respectively corresponding to the plurality of feature values of the feature vector;
training a prediction model utilizing the plurality of explanatory variable subsets, the different weight coefficients, the feature vector, the regression vector, and an objective function to be reduced, the objective function comprising a weighted sum of terms indicating errors between labels predicted for the plurality of explanatory variable subsets based on the feature vector and the regression vector, and the label allocated to the set of explanatory variables the set of training data, wherein the prediction model generates a predicted label to be allocated to a prediction target explanatory variable set with each of the plurality of explanatory variable subsets allocated a different weight coefficient and a regression vector comprising a plurality of regression coefficients respectively corresponding to the plurality of feature values of the feature vector and the different weight coefficient of each of the plurality of explanatory variables; and
outputting a posterior probability distribution of the regression vector and the different weight coefficients as a training result.