Impossibility Results for Distributed Computing: Discussions, Guidelines, and Examples for Artificial Intelligence - Couverture rigide
Synopsis
This book presents the first unified formalization for defining novelty across the span of machine learning, symbolic-reasoning, and control and planning-based systems. Dealing with novelty, things not previously seen by a system, is a critical issue for building vision-systems and general intelligent systems. The book presents examples of using this framework to define and evaluate in multiple domains including image recognition image-based open world learning, hand-writing and author analysis, CartPole Control, Image Captioning, and Monopoly. Chapters are written by well-known contributors to this new and emerging field. In addition, examples are provided from multiple areas, such as machine-learning based control problems, symbolic reasoning, and multi-player games.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Terrance E. Boult, Ph.D., is a Distinguished Professor and El Pomar Endowed Professor of Innovation and Security in the Department of Computer Science at the University of Colorado at Colorado Springs. He is also an IEEE Fellow and an internationally acknowledged researcher in machine learning, computer vision, biometrics, and cybersecurity with 15 patents issued and 400+ articles. Dr. Boult received the B.S. degree in Applied Mathematics, the M.S. degree in Computer Science, and the Ph.D. degree in Computer Science from Columbia University. He has won multiple teaching awards, research/innovation awards, best paper awards, best reviewer awards, and IEEE service awards.
Walter Scheirer, Ph.D., is Dennis O. Doughty Collegiate Associate Professor in the Department of Computer Science and Engineering at the University of Notre Dame. Previously, he was a postdoctoral fellow at Harvard University with affiliations in the Schoolof Engineering and Applied Sciences, Department of Molecular and Cellular Biology, and Center for Brain Science. Dr. Scheirer received his Ph.D. from the University of Colorado and his M.S. and B.A. degrees from Lehigh University. He has extensive experience in the areas of computer vision, machine learning, and image processing. His current research is focused on media forensics and studying disinformation circulating on social media.Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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A Unifying Framework for Formal Theories of Novelty
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A Unifying Framework for Formal Theories of Novelty (Hardcover)
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ISBN 13 : 9783031330537
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Hardcover. Etat : new. Hardcover. This book presents the first unified formalization for defining novelty across the span of machine learning, symbolic-reasoning, and control and planning-based systems. Dealing with novelty, things not previously seen by a system, is a critical issue for building vision-systems and general intelligent systems. The book presents examples of using this framework to define and evaluate in multiple domains including image recognition image-based open world learning, hand-writing and author analysis, CartPole Control, Image Captioning, and Monopoly. Chapters are written by well-known contributors to this new and emerging field. In addition, examples are provided from multiple areas, such as machine-learning based control problems, symbolic reasoning, and multi-player games. This book presents the first unified formalization for defining novelty across the span of machine learning, symbolic-reasoning, and control and planning-based systems. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9783031330537
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A Unifying Framework for Formal Theories of Novelty : Discussions, Guidelines, and Examples for Artificial Intelligence
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ISBN 10 : 3031330536
ISBN 13 : 9783031330537
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