Neural Classifier

Neural classifiers are machine learning models designed to categorize data into predefined classes, aiming for high accuracy and robust generalization. Current research emphasizes improving interpretability through techniques like image decomposition and integrating decision trees into neural network architectures, alongside investigating the internal dynamics of these networks, such as neural collapse phenomena in hidden layers. These advancements address limitations in existing models, including computational cost, lack of transparency, and vulnerability to adversarial attacks, ultimately enhancing the reliability and applicability of neural classifiers across diverse fields.