Human and Machine Learning : Visible, Explainable, Trustworthy and Transparent

Fang Chen

ISBN 10: 3319904027 / ISBN 13: 9783319904023

Edité par Springer International Publishing, Springer International Publishing, 2018

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9783319904023

Détails bibliographiques

• Titre: Human and Machine Learning : Visible, Explainable, Trustworthy and Transparent
• Éditeur: Springer International Publishing, Springer International Publishing
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 3319904027
• ISBN à 13 chiffres: 9783319904023
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 241 L x 160 H x 33 P (en millimètres) 241 millimètres de largeur par 160 millimètres de hauteur par 33 millimètres de profondeur
• Poids: 922 grammes

A propos de ce titre

Synopsis

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.

À propos de l?auteur

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 andmonitor 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.