Human and Machine Learning : Visible, Explainable, Trustworthy and Transparent / edited by Jianlong Zhou, Fang Chen.
Material type: TextSeries: Human-Computer Interaction SeriesPublisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018Description: 1 online resource (XXIII, 482 pages 140 illustrations, 114 illustrations in color.)Content type:- text
- computer
- online resource
- 9783319904030
- 005.437 23 HUM
- 4.019 23
Item type | Current library | Call number | Copy number | Status | Date due | Barcode |
---|---|---|---|---|---|---|
Books | Central Library, SUST General Stacks | 005.437 HUM (Browse shelf(Opens below)) | 1 | Available | 0077589 |
Browsing Central Library, SUST shelves, Shelving location: General Stacks Close shelf browser (Hides shelf browser)
No cover image available No cover image available | ||||||||
005.437 GAM Game analytics : | 005.437 GAM Game analytics : | 005.437 GAM Game analytics : | 005.437 HUM Human and Machine Learning : Visible, Explainable, Trustworthy and Transparent / | 005.437 LEM MCSD: windowsarchitecture II study guide / | 005.43769 EZN NT 4/Windows 95 developer's handbook / | 005.43769 WYW Windows NT workstation 4.0 bible / |
Part I Transparency in Machine Learning -- Part II Visual Explanation of Machine Learning Process -- Part III Algorithmic Explanation of Machine Learning Models -- Part IV User Cognitive Responses in ML-Based Decision Making -- Part V Human and Evaluation of Machine Learning -- Part VI Domain Knowledge in Transparent Machine Learning Applications.
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 explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.
Description based on publisher-supplied MARC data.
There are no comments on this title.