多源数据学习在大数据时代具有极其重要的意义.目前，多源数据学习算法研究远远超前于多源数据学习理论研究，经典的机器学习理论难以应用于多源数据学习，更难以提供多源数据学习算法在实际应用中的理论保障.从学习的最终目的是知识这一认知切入点出发，对人类学习的认知机理、机器学习的三大经典理论（计算学习理论、统计学习理论和概率图理论）以及多源数据学习算法设计这3个方面的研究进展进行总结，最后给出未来研究方向的思考.

In the age of big data, learning from multi-source data plays an important role in many real applications. To date, plenty of multi-source data learning algorithms have been proposed, however, they pay little attention to the fundamental theoretic laws. Meanwhile, it is hard for the classical machine learning theories to govern all learning systems, and to further provide a theoretical support for multi-source learning algorithms. From the perspective of knowledge acquisition through learning, a survey is given on the research progress of three key problems:the human cognitive mechanism, three classical machine learning theories (such as computational learning theory, statistical learning theory, and probabilistic graphical model), and the design of multi-source learning algorithms. Future theoretical research issues of multi-source data learning also presented and investigated.

国家自然科学基金（61632004，61773198，61702358）