ISBN: 9783319504773 - machine learning for health informatics: state-of-the-art and future challenges (lecture notes in artificial intelligence, band 9605) (27 Ergebnisse)

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Paperback. Zustand: New. 1st ed. 2016. Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field. …

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Kartoniert / Broschiert. Zustand: New. Hot topics in machine learning for health informaticsState-of-the-art survey and output of the international HCI-KDD expert networkDiscusses open problems and future challenges in order to stimulate further research and international progr.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization.Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence.This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.…

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Taschenbuch. Zustand: Neu. Machine Learning for Health Informatics | State-of-the-Art and Future Challenges | Andreas Holzinger | Taschenbuch | Lecture Notes in Computer Science | xxii | Englisch | 2016 | Springer | EAN 9783319504773 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Paperback. Zustand: new. Paperback. Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field. Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Paperback. Zustand: New. 1st ed. 2016. Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field. …

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Paperback. Zustand: new. Paperback. Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field. Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …

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Paperback. Zustand: Brand New. 504 pages. 9.00x6.00x1.25 inches. In Stock. This item is printed on demand.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization.Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence.This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field. 504 pp. Englisch. …

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization.Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence.This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 504 pp. Englisch. …

Zustand: New. Print on Demand pp. 481.

Zustand: New. PRINT ON DEMAND pp. 481.