TY - BOOK ID - 32940022 TI - Human and Machine Learning : Visible, Explainable, Trustworthy and Transparent AU - Zhou, Jianlong. AU - Chen, Fang. PY - 2018 SN - 3319904035 3319904027 9783319904023 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Computer science. KW - User interfaces (Computer systems). KW - Artificial intelligence. KW - Pattern recognition. KW - Computer Science. KW - User Interfaces and Human Computer Interaction. KW - Artificial Intelligence (incl. Robotics). KW - Pattern Recognition. KW - Design perception KW - Pattern recognition KW - Form perception KW - Perception KW - Figure-ground perception KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Interfaces, User (Computer systems) KW - Human-machine systems KW - Human-computer interaction KW - Informatics KW - Science KW - Optical pattern recognition. KW - Artificial Intelligence. KW - Optical data processing KW - Pattern perception KW - Perceptrons KW - Visual discrimination KW - Machine learning. KW - Learning. KW - Human-computer interaction. UR - https://www.unicat.be/uniCat?func=search&query=sysid:32940022 AB - 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. ER -