杠杆(统计)
学习迁移
计算机科学
过程(计算)
人工智能
机器学习
知识转移
知识管理
操作系统
出处
期刊:Royal Society of Chemistry eBooks
[The Royal Society of Chemistry]
日期:2023-12-20
卷期号:: 229-246
标识
DOI:10.1039/bk9781837670178-00229
摘要
Transfer learning provides an effective and practical solution to modelling novel systems when a lack of theoretical understanding and data availability hinders progress. In this chapter, transfer learning aims to leverage previously discovered relations and prior understanding of complex biochemical systems to support the rapid construction of accurate predictive models for different but related biochemical systems. This chapter explores the application and advantages of transfer learning for a real experimental case study to demonstrate the potential of transfer learning within the biochemical industry. To maximise the use of available process knowledge, transfer learning and hybrid modelling are combined for the first time. Building on the hybrid modelling methodology introduced in Chapter 3, a step-by-step explanation is provided for transfer-hybrid model construction, focusing on the selection and implementation of the chosen transfer learning approach and the decision about which aspects of the model to transfer or update for the new system to avoid inheriting domain-specific biases. The study concludes by comparing the accuracy and uncertainty of the transfer-hybrid model with a traditional-hybrid model. Although the results are case-specific, they provide valuable evidence that transfer learning can accelerate biochemical process model construction and help bolster innovation when correctly employed.
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