Автор: Calvaresi Davide, Najjar Amro, Winikoff Michael Название: Explainable, Transparent Autonomous Agents and Multi-Agent Systems: Second International Workshop, Extraamas 2020, Auckland, New Zealand, May 9-13, 20 ISBN: 3030519236 ISBN-13(EAN): 9783030519230 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the proceedings of the Second International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems, EXTRAAMAS 2020, which was due to be held in Auckland, New Zealand, in May 2020.
Название: Human and machine learning ISBN: 3319904027 ISBN-13(EAN): 9783319904023 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Автор: Zhou Jianlong, Chen Fang Название: Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent ISBN: 3030080072 ISBN-13(EAN): 9783030080075 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Автор: Shaban-Nejad Arash, Michalowski Martin, Buckeridge David L. Название: Explainable AI in Healthcare and Medicine: Building a Culture of Transparency and Accountability ISBN: 3030533514 ISBN-13(EAN): 9783030533519 Издательство: Springer Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book highlights the latest advances in the application of artificial intelligence and data science in health care and medicine.
Автор: Mehta Название: Explainable AI: Foundations, Methodologies and Applications ISBN: 3031128060 ISBN-13(EAN): 9783031128066 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.
This book focuses on an overview of the AI techniques, their foundations, their applications, and remaining challenges and open problems. Many artificial intelligence (AI) techniques do not explain their recommendations. Providing natural-language explanations for numerical AI recommendations is one of the main challenges of modern AI. To provide such explanations, a natural idea is to use techniques specifically designed to relate numerical recommendations and natural-language descriptions, namely fuzzy techniques.
This book is of interest to practitioners who want to use fuzzy techniques to make AI applications explainable, to researchers who may want to extend the ideas from these papers to new application areas, and to graduate students who are interested in the state-of-the-art of fuzzy techniques and of explainable AI--in short, to anyone who is interested in problems involving fuzziness and AI in general.
Название: Explainable artificial intelligence for smart cities ISBN: 1032001127 ISBN-13(EAN): 9781032001128 Издательство: Taylor&Francis Рейтинг: Цена: 102070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The objective of this book is to focus on Explainable Artificial Intelligence (XAI) in smart city development. This book provides a timely, global reference source about cutting edge research efforts to ensure the XAI factor in smart city-oriented developments.
Автор: Singh, Java Название: Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligence (RAI) ISBN: 9811914753 ISBN-13(EAN): 9789811914751 Издательство: Springer Рейтинг: Цена: 167700.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The book discusses Explainable (XAI) and Responsive Artificial Intelligence (RAI) for biomedical and healthcare applications. The book explains both positive as well as negative findings obtained by explainable AI techniques.
Автор: Gianfagna Leonida, Di Cecco Antonio Название: Explainable AI with Python ISBN: 3030686396 ISBN-13(EAN): 9783030686390 Издательство: Springer Рейтинг: Цена: 65210.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Model-agnostic methods for XAI are shown to produce explanations without relying on ML models internals that are "opaque." Using examples from Computer Vision, the authors then look at explainable models for Deep Learning and prospective methods for the future.
Автор: Rothman Denis Название: Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps ISBN: 1800208138 ISBN-13(EAN): 9781800208131 Издательство: Неизвестно Рейтинг: Цена: 67430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In today`s era of AI, accurately interpreting and communicating trustworthy, fair, and secure AI findings have become a crucial skill to master. This book bridges the gap between AI`s pitfalls and potential by helping you build the ability to leverage machine learning with Python to visualize and integrate AI.
Автор: Edited By Bhavnesh Kumar, Bhanu Pratap, Vivek Shri Название: Artificial Intelligence for Solar Photovoltaic Systems ISBN: 1032054417 ISBN-13(EAN): 9781032054414 Издательство: Taylor&Francis Рейтинг: Цена: 132710.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides a clear explanation of the application of artificial intelligence to solve the challenges in solar photovoltaic technology. It introduces the readers about new AI-based approaches and technologies helpful in managing and operating solar photovoltaic systems effectively.
Автор: Dombi Jуzsef, Csiszбr Orsolya Название: Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools ISBN: 3030722821 ISBN-13(EAN): 9783030722821 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The research presented in this book shows how combining deep neural networks with a special class of fuzzy logical rules and multi-criteria decision tools can make deep neural networks more interpretable - and even, in many cases, more efficient.
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