Square Loss

Square loss, a fundamental measure of error in regression and classification problems, is experiencing renewed interest in machine learning research. Current investigations focus on mitigating its sensitivity to outliers and noise through alternative loss functions within various neural network architectures, such as RVFL networks, and on improving the efficiency of optimization algorithms like coordinate descent. This research aims to enhance the robustness and scalability of machine learning models, particularly in noisy or high-dimensional data settings, leading to more reliable and efficient predictions across diverse applications.