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领域（源领域）的现有模型来学习一个领域（目标领域）的模型的问题。关键问题是如何使源领域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（源BN）的BN进行操作以获得目标域（目标BN）的新BN是很自然的。因此，bp网络适用于迁移学习的场景。另一方面，尽管人们对神经网络进行了几十年的深入研究，但学习神经网络仍然具有相当大的挑战性，尤其是在训练数据稀缺的情况下。迁移学习方法可以为克服这一问题提供一种有效的方法。

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.