Jianlong Zhou (86 results)

Condition: New. pp. VIII, 138 49 illus., 31 illus. in color. 2020th edition NO-PA16APR2015-KAP.

paperback. Condition: Gut. 505 Seiten; 9783030080075.3 Gewicht in Gramm: 1.

Condition: New. In.

Paperback. Condition: New.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Soft cover. Condition: Fine. No Jacket. 1 v. (unpaged) : ill. ; 27 cm. ; Sale code: 5948 WU BIN/ Place of sale: New York ; OCLC: 171301984 ; color photographic stiff paper wrappers ; prices realized laid in ; features works by Zhao Mengfu, Wu Zhen, Zhao Yong, Yang Weizhen, Shen Zhou, Lu Ji, Zhang Lu, Tang Yin, Zhan Ruoshui, Wen Zhengming, Zhu Yunming, Wang Chong, Xie Shichen, Wen Jia, Qian Gu, Zhou Tianqiu, Wu Bin, Song Moujin, Chen Jiayan, Li Liufang, Zhang Kunlin, Dong Qichang, Chen Jiru, Zhao Zuo, Zhao Zun, Jiang Ai, Li Shida, Shang Maoye, Chen Guan, Wang Weilie, Zhang Ruitu, Huang Daozhou, Ni Yuanhu, Xiao Yuncong, Lan Ying, Zhang Feng, Wang Jian, Wang Shimin, Gu Jianlong, Wang Wu, Zha Shibiao, Gong Xian, Ma Futu, Yun Shouping, Luo Mu, Zu Da (Bada Shanren), Daoji, Chen Yixi, Sun Yi, Yu Zhiding, Wang Yuanqi, Wang Gai, Wang Hui, Ma Quan, Xu Bin, Qing Shengzu ; FINE. Book.

Condition: NEW.

Condition: As New. Unread book in perfect condition.

Condition: New.

Hardcover. Condition: New.

Condition: New. In.

Hardcover. Condition: new. Hardcover. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: New.

Hardback. Condition: New. 2018 ed. 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.

Condition: As New. Unread book in perfect condition.

Condition: NEW.

Condition: Sehr gut. Zustand: Sehr gut | Seiten: 508 | Sprache: Englisch | Produktart: Bücher | 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 żblack-boxż 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.

Taschenbuch. Condition: Neu. Gaining Insights Into Volumetric Data Visualization | A Semi-Automatic Transfer Function Generation Approach Using Contour Tree Analyses | Jianlong Zhou | Taschenbuch | 184 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659134845 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condition: Neu. Neuware -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 żblack-boxż 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 508 pp. Englisch.

Hardback. Condition: New. 2018 ed. 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.

Buch. Condition: Neu. Neuware -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. 508 pp. Englisch.

Taschenbuch. Condition: Neu. 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.

Condition: New.

Taschenbuch. Condition: Neu. Human and Machine Learning | Visible, Explainable, Trustworthy and Transparent | Jianlong Zhou (u. a.) | Taschenbuch | Human-Computer Interaction Series | xxiii | Englisch | 2019 | Springer | EAN 9783030080075 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Condition: New. 1st ed. 2018 edition NO-PA16APR2015-KAP.

Condition: New.

Paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Gebunden. Condition: New. Creates a systematic view of relations between human and machine learning from the perspectives of visualisation, explanation, trustworthiness and transparencyExplores human aspects in machine learning based on algorithms, human cognitive response.