Editors:
Presents the current state of the art on combining artificial intelligence and machine learning with visual analytics
Presents research work on computational intelligence, machine learning, visual analytics, and knowledge discovery
Covers integrated systems, supervised learning, and unsupervised learning
Part of the book series: Studies in Computational Intelligence (SCI, volume 1014)
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Table of contents (26 chapters)
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Front Matter
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Machine Learning and Visualization
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Front Matter
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About this book
This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain.
This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations.
The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.
Keywords
- Computational Intelligence
- Artificial Intelligence
- Machine Learning
- Visual Analytics
- Knowledge Discovery
Editors and Affiliations
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Department of Computer Science, Central Washington University, Ellensburg, USA
Boris Kovalerchuk, Răzvan Andonie
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Department of Media, Darmstadt University of Applied Sciences, Darmstadt, Germany
Kawa Nazemi
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Department of Electronics, Telecommunications and Computers Engineering, Lisbon School of Engineering, Lisbon, Portugal
Nuno Datia
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Department of Informatics, London South Bank University, London, UK
Ebad Banissi
Bibliographic Information
Book Title: Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery
Editors: Boris Kovalerchuk, Kawa Nazemi, Răzvan Andonie, Nuno Datia, Ebad Banissi
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-93119-3
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-93118-6Published: 05 June 2022
Softcover ISBN: 978-3-030-93121-6Due: 19 June 2023
eBook ISBN: 978-3-030-93119-3Published: 04 June 2022
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XV, 674
Number of Illustrations: 46 b/w illustrations, 288 illustrations in colour