Strong Learner

Strong learners, in machine learning, aim to achieve high accuracy by combining multiple weaker models. Current research focuses on improving the efficiency and robustness of strong learners, exploring techniques like boosting algorithms (including AdaBoost and its variants), curriculum learning (adapting training difficulty), and knowledge distillation (transferring knowledge from a strong "teacher" model to a weaker "student"). These advancements are impacting various fields, from improving educational tools through AI-driven error correction to enhancing semi-supervised learning in computer vision and natural language processing.