Cross-domain knowledge transfer for data-driven decision making

该项目旨在开发一套跨领域知识转移方法，以支持数据驱动决策（D3M）系统。D3M在商业中是必不可少的，特别是在当今大数据时代不断变化的环境中，但用于解决新问题的D3M可能面临域内数据不足的问题。挑战在于如何有效地从多个异构源领域转移知识。研究结果有望转移隐性和显性知识，处理离散和连续输出，并支持业务决策，这将推动迁移学习和数据驱动的决策支持系统在动态变化的环境中的发展。

This project aims to develop a set of cross-domain knowledge transfer methodologies to support Data-Driven Decision-Making (D3M) systems. D3M is essential in business, particularly for ever-changing environments in today’s big data era, but D3Ms for solving new problems may face in-domain data insufficiency. The challenge is to effectively transfer knowledge from multiple heterogeneous source domains. The outcomes are expected to transfer implicit and explicit knowledge, handle discrete and continuous outputs, and support business decision-making, which should advance the discipline of transfer learning and data-driven DSS in dynamically changing environments.