原文地址：http://www2.le.ac.uk/departments/informatics/research/kdml

Potential Collaborative Projects with Industry

Within KDML we have a broad range of research interests and capabilities. Below are some examples of current projects. If you have any queries or ideas, please do not hesitate to contact us (see below for contact details).

Effort-estimation from cross-company data: We have developed Machine Learning algorithms that can enable organisations to accurately predict effort by using cross-company data, reducing the dependence upon internally recorded data.

Textile flaw detection: We have had a successful series of collaborations with an industrial partner in the textile industry. As a part of this, we inferred classifiers to more accurately detect textile flaws, flagging up fewer false-positives, and leading to a higher degree of automation.

Analysing live data streams to predict rail traffic build-up: The DfT funded PREPAReD project is fusing live rail data with computational models to enable the prediction of rail delays.

Multi-factor decision support for software safety case assessments: We have developed a tool-supported approach to aggregate multi-faceted safety assessments for critical software components, and to produce coherent overviews.

【转】机器学习在工业中的应用场景