Patent ID: 10832174

Abstract:
Data is classified using automatically selected hyperparameter values. (A) A first loss value is determined based on a converged classification matrix. (B) Each observation vector is assigned to a cluster using a clustering algorithm based on the converged classification matrix. (C) A predefined number of observation vectors is selected from each cluster. D) Classified observation vectors and unclassified observation vectors are updated based on the selections in (C) and (A) is repeated. (E) An entropy loss value is determined, wherein (A) to (E) are repeated for a plurality of different values of a kernel parameter value and a batch size value. (F) A second loss value is determined based on the converged classification matrix, a label matrix defined from the converged classification matrix, and a weight value. (L) (A) to (F) are repeated with a plurality of different values of the weight value until convergence is satisfied.