Abstract:
An artificial intelligence (AI) classifier is trained using supervised training and an effect of noise in the training data is reduced. The training data includes observed noisy labels. A posterior transition matrix (PTM) is used to minimize, in a statistical sense, a cross entropy between a noisy label and a function of the classifier output. A loss function using the PTM is provided to use in training the classifier. The classifier provides final output predictions with good performance even with the existence of noisy labels. Also, information fusion is included in the classifier training using the PTM and an estimated noise transition matrix (NTM) to reduce estimation error at the classifier output.
Abstract:
A method and an apparatus for analyzing source codes of an application program having open source codes and analyzing features which are used in the application program are provided. The method includes analyzing the application program according to the source codes in the application program, determining application program configuration information used in the application program, and classifying and outputting the application program configuration information according to the determined application program configuration information.