Passive Learning

Passive learning, a machine learning paradigm where models are trained on randomly selected data, is being actively investigated for its efficiency and cost-effectiveness, particularly when labeled data is scarce or expensive. Current research focuses on improving passive learning's performance through techniques like uncertainty sampling (refined with methods such as bell curve sampling) and by exploring its application in diverse areas such as natural language processing (e.g., RAG models) and cost-sensitive classification. These advancements aim to enhance the accuracy and efficiency of passive learning, offering a valuable alternative to more resource-intensive active learning approaches in various applications, including those with non-stationary data or high-dimensional feature spaces.