國立虎尾科技大學 |

Human and machine learning = visible, explainable, trustworthy and transparent /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Human and machine learning/ edited by Jianlong Zhou, Fang Chen.
Reminder of title:	visible, explainable, trustworthy and transparent /
other author:	Zhou, Jianlong.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	xxiii, 482 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Computer science. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-90403-0
ISBN:	9783319904030

Human and machine learning = visible, explainable, trustworthy and transparent /
Human and machine learningvisible, explainable, trustworthy and transparent /[electronic resource] :edited by Jianlong Zhou, Fang Chen. - Cham :Springer International Publishing :2018. - xxiii, 482 p. :ill., digital ;24 cm. - Human-computer interaction series,1571-5035. - Human-computer interaction series..

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.

ISBN: 9783319904030

Standard No.: 10.1007/978-3-319-90403-0doiSubjects--Topical Terms:

573171
Computer science.

LC Class. No.: QA76.9.H85

Dewey Class. No.: 006.31

Human and machine learning = visible, explainable, trustworthy and transparent /
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520 $a 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.
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