Transfer learning for Bayesian networks

贝叶斯网络的迁移学习

• 批准号: 261282-2012
• 负责人: Zhang, Huajie
• 金额: $ 1.24万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=507983
• 关键词: Transfer; learning; Bayesian; networks

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.

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(源域)的现有模型来学习领域(目标领域)的模型的问题。关键问题是如何调整源域中的现有知识以适应目标域。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执行操作以获得目标域(目标BN)的新BN是很自然的。因此，贝叶斯网络适用于迁移学习场景。另一方面，尽管贝叶斯网络已经被深入研究了几十年，但学习贝叶斯网络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学习BN中的这一问题。