Notice bibliographique
- Notice
000 02771c0 m 22000271 45as
001 FRBNF457789910000008
008 200203s 2018 cheng b 001
009 a
009 sa 1 a mm x
017 .. $o OCoLC $a 1132041023 $k LVB $l fre $m LVB $n rda
020 .. $a 9783319904030 $a 3319904035 $z 9783319904023 $z 3319904027 $a 3319904027 $a 9783319904023
051 .. $a txt $b c
245 1. $a Human and machine learning $d Texte électronique $e visible, explainable, trustworthy and transparent $f editors Jianlong Zhou, Fang Chen
260 .1 $a Cham $c Springer $i 2018
280 .. $a 1 ressource dématérialisée
295 1. $a Human-computer interaction series
300 .. $a Notes bibliogr.
330 .. $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
829 1. $a 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.
