Search Results for author:
Found 2 papers, 0 papers with code
A Quantitative Comparison between Shannon and Tsallis Havrda Charvat Entropies Applied to Cancer Outcome Prediction
In this paper, we propose to quantitatively compare loss functions based on parameterized Tsallis-Havrda-Charvat entropy and classical Shannon entropy for the training of a deep network in the case of small datasets which are usually encountered in medical applications.
Deep learning using Havrda-Charvat entropy for classification of pulmonary endomicroscopy
Our method is tested on one POE dataset including 2947 distinct images, is showing better results than using Shannon entropy and behaves better with regard to the problem of overfitting.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
