Human and Machine Learning
Visible, Explainable, Trustworthy and Transparent
With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of black-box in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explan…
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Produktdetails
Weitere Autoren: Chen, Fang (Hrsg.)
- ISBN: 978-3-319-90402-3
- EAN: 9783319904023
- Produktnummer: 27060576
- Verlag: Springer-Verlag GmbH
- Sprache: Englisch
- Erscheinungsjahr: 2018
- Seitenangabe: 482 S.
- Masse: H24.1 cm x B16.0 cm x D3.3 cm 922 g
- Auflage: 2018
- Abbildungen: Book; Bibliographie
- Gewicht: 922
Über den Autor
Dr. Jianlong Zhou's research interests include interactive behaviour analytics, human-computer interaction, machine learning, and visual analytics. He has extensive experience in data driven multimodal cognitive load and trust measurement in predictive decision making. He leads interdisciplinary research on applying visualization and human behaviour analytics in trustworthy and transparent machine learning. He also works with industries in advanced data analytics for transforming data into actionable operations, particularly by incorporating human user aspects into machine learning to translate machine learning into impacts in real world applications.Dr. Fang Chen works in the field of behaviour analytics and machine learning in data driven business solutions. She pioneered the theoretical framework of multimodal cognitive load measurement, and provided much of the empirical evidence on using human behaviour signals and physiological responses to measure and monitor cognitive load. She also leads many taskforces in applying advanced data analytic techniques to help industries make use of data, leading to improved productivity and innovation through business intelligence. Her extensive experience on cognition and machine learning applications across different industries brings unique insights on bridging the gap of machine learning and its impact.
4 weitere Werke von Jianlong (Hrsg.) Zhou:
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