Enhancing cross entropy with a linearly adaptive loss function for optimized classification performance

Abstract We propose the linearly adaptive cross entropy loss function. This is a novel measure derived from the information theory. In comparison to the standard cross entropy loss function, the proposed one has an additional term that depends on the predicted probability of the true class. This fea...

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Bibliographic Details
Main Author: Jae Wan Shim
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
Subjects:
Online Access:https://doi.org/10.1038/s41598-024-78858-6
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