Online Machine Learning: Challenges, and Opportunities

Online Machine Learning (OML) is an approach, for managing data and training models, beneficial in sectors like automation, education and healthcare. It allows for real time adjustments and accurate predictions based on data. OML models are regularly updated with data to enhance efficiency and scalability. Adaptive models such as Adaptive Random Forest (ARF) and K Nearest Neighbors (KNN) are utilized to handle streaming data and adapt to emerging trends. Dynamic learning methods utilize information to tailor learning experiences and cater to the evolving needs of students. Common algorithms include learning, batch learning and test time adaptation. Adaptive models like online Support Vector Machines (SVMs) overcome the limitations of machine learning approaches by adjusting to data distributions. Online decision tree models, incremental clustering techniques, online random forests and Hoeffding trees are employed for real time forecasting and classification. Nevertheless OML encounters challenges such as system theory definitions, high operational costs, maintenance expenses and anomaly detection issues. To tackle these challenges effectively strategies, like learning, adaptive testing approaches and active online learning techniques are recommended. The above article is a review based on reputable scientific papers aimed at identifying the challenges and opportunities of online machine learning.

کلیدواژه ها:

Online Machine Learning ، Machine Learning ، Machine Learning Techniques and Algorithms