HasLoss: a novel Hassanat distance-based loss functions for binary classification

This work establishes both theoretical foundations and empirical validation for distance-based loss functions. The bounded gradient framework with finite Lipschitz constants provides principled optimization guarantees while explaining observed robustness and convergence behavior. This foundation enables systematic development of robust loss functions tailored to specific application requirements.