Fuzzy Transfer Learning for Prediction in Data-Shortage and Rapidly-Changing Environments

用于数据短缺和快速变化环境中预测的模糊迁移学习

• 批准号: DP140101366
• 负责人: Prof Jie Lu
• 金额: $ 27.55万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2014
• 资助国家: 澳大利亚
• 起止时间: 2014-01-01 至 2017-12-20
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP140101366
• 关键词: Fuzzy; Transfer; Learning; Prediction; Data

Collecting sufficient up-to-date data to train a learning model for data analysis and prediction is difficult and expensive. This project will develop a Fuzzy Transfer Learning methodology, using Information Granularity theory, that exploits data with different features and/or distributions available in other, similar systems, to provide accurate learning-based prediction for current problems. It will establish a new research direction, Fuzzy Transfer Learning for Prediction, and the outcomes will enable government and industry to better use past experience to make more accurate predictions and decisions. Highly significant benefits will also accrue in the data analytics, business intelligence and decision making research fields.

收集足够的最新数据来训练数据分析和预测的学习模型是困难和昂贵的。该项目将利用信息粒度理论开发一种模糊转移学习方法，利用其他类似系统中具有不同特征和/或分布的数据，为当前问题提供准确的基于学习的预测。它将建立一个新的研究方向，即用于预测的模糊转移学习，其结果将使政府和行业能够更好地利用过去的经验做出更准确的预测和决策。在数据分析、商业智能和决策研究领域也将产生非常显著的好处。