Transfer learning for Bayesian networks

迁移学习解决了如何通过利用来自另一个不同但相关的域（源域）的现有模型来学习域（目标域）的模型的问题。关键问题是如何使源领域中已有的知识适应目标领域。因此，领域知识的表示起着重要的作用。贝叶斯网络（BN）是领域知识的显式图形表示。对源域的BN（源BN）执行操作以获得目标域的新BN（目标BN）是自然的。因此，BN适合于迁移学习的场景。另一方面，尽管BN已经被深入研究了几十年，但学习BN仍然具有相当大的挑战性，特别是在训练数据稀缺的情况下。迁移学习方法可以提供一种有效的方法来克服这个问题在学习BN。

Transfer learning addresses the problem of how to learn a model for a domain (target domain) by utilizing an existing model from another different but related domain (source domain). The key issue is how to adapt the existing knowledge in source domain to fit target domain. Thus the representation of domain knowledge plays an important role. A Bayesian network (BN) is an explicit graphical representation of domain knowledge. It is natural to perform manipulations on a BN for source domain (source BN) to obtain a new BN for target domain (target BN). Therefore, BNs are suitable in the scenario of transfer learning. On the other hand, although BNs have been intensively studied for decades, learning BNs still remains quite challenging, especially when training data is scarce. The transfer learning approach can provide an effective way to overcome this issue in learning BNs.