Softmax Classifier

The softmax classifier is a fundamental component of many machine learning models, primarily used for multi-class classification by assigning probabilities to different classes. Current research focuses on improving its explainability, addressing limitations in handling out-of-distribution data, and enhancing its performance within specific architectures like transformers and convolutional neural networks. These efforts aim to improve model accuracy, generalization, and interpretability across diverse applications, including image recognition, natural language processing, and medical image analysis, leading to more robust and reliable classification systems.