Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent
暫譯: 人類與機器學習：可見、可解釋、可信賴與透明

Zhou, Jianlong, Chen, Fang

Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent
  • 出版商: Springer
  • 出版日期: 2019-01-10
  • 售價: $3,050
  • 貴賓價: 9.5 折 $2,898
  • 語言: 英文
  • 頁數: 482
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3030080072
  • ISBN-13: 9783030080075
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

商品描述

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.


商品描述(中文翻譯)

隨著機器學習（Machine Learning, ML）演算法的進化、數據量的快速增長以及計算能力的顯著提升，機器學習在各種應用中變得越來越熱門。然而，由於ML方法的「黑箱」特性，機器學習仍然需要被解釋，以便將人類與機器學習聯繫起來，從而提高所提供解決方案的透明度和用戶接受度。本書從可視化、解釋、可信度和透明度的角度探討了這些聯繫。該書通過探索機器學習的透明性、ML過程的可視化解釋、ML模型的演算法解釋、基於ML的決策中的人類認知反應、人類對機器學習的評估以及透明ML應用中的領域知識，建立了人類與機器學習之間的聯繫。

這是第一本系統性理解與人類和機器學習相關的當前活躍研究活動和成果的書籍。該書不僅將激勵研究人員熱情地開發新演算法，將人類納入以人為中心的ML演算法，從而促進機器學習的整體進步，還將幫助機器學習從業者主動使用ML輸出進行信息豐富且可信的決策。

本書旨在為從事機器學習及其應用的研究人員和從業者提供參考。該書將特別惠及人工智慧、決策支持系統和人機互動等領域的研究人員。

作者簡介

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.


作者簡介(中文翻譯)

周建龍博士的研究興趣包括互動行為分析、人機互動、機器學習和視覺分析。他在數據驅動的多模態認知負荷和信任測量方面擁有豐富的經驗，特別是在預測決策中。他領導跨學科的研究，應用可視化和人類行為分析於可信且透明的機器學習中。他還與產業合作，進行先進的數據分析，將數據轉化為可行的操作，特別是通過將人類用戶的方面納入機器學習，將機器學習轉化為在現實世界應用中的影響。

陳芳博士專注於行為分析和數據驅動商業解決方案中的機器學習。她開創了多模態認知負荷測量的理論框架，並提供了大量實證證據，使用人類行為信號和生理反應來測量和監控認知負荷。她還領導多個專案小組，應用先進的數據分析技術，幫助產業利用數據，從而通過商業智慧提高生產力和創新。她在不同產業中對認知和機器學習應用的豐富經驗，為縮小機器學習與其影響之間的差距提供了獨特的見解。

類似商品