Weak Learning

Weak learning focuses on training machine learning models using limited or imperfect data, aiming to build strong classifiers by combining multiple weaker ones. Current research investigates the fundamental limits of weak learnability, particularly within high-dimensional data and using models like AdaBoost and Random Forests, exploring how factors like sample complexity and data distribution affect performance. This research is crucial for advancing machine learning in scenarios with scarce labeled data or noisy signals, impacting diverse applications from natural language processing and computer vision to healthcare and cybersecurity.