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Adaptive Asymmetric Loss Function for Positive Unlabeled Learning
Unlike traditional semi-supervised learning, positive unlabeled learning requires only some labeled data from the positive class. All other data, both positive and negative, is unlabeled. The goal is the same, i.e., to construct a classification model that correctly labels unlabeled images and create a model to label future images. This problem is...
Published: 10/25/2023   |   Inventor(s): Kristen Jaskie, Nolan Vaughn, Vivek Sivaraman Narayanaswamy, Sahba Zaare, Joseph Marvin, Andreas Spanias
Keywords(s): Algorithm Development, PS-Computing and Information Technology
Category(s): Computing & Information Technology, Physical Science