Classification Loss Function

Classification loss functions guide the training of machine learning models by quantifying the discrepancy between predicted and true labels. Current research focuses on improving existing functions like cross-entropy and exploring alternatives such as those based on polynomial expansions or incorporating multi-stage feature decorrelation to enhance model accuracy and generalization, particularly within convolutional neural networks. These advancements aim to address limitations of traditional approaches, such as susceptibility to local optima and poor performance on non-linearly separable data, leading to more robust and efficient classification models across various applications.