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Condition: New. 1st ed. 2018 edition NO-PA16APR2015-KAP.
Seller: Revaluation Books, Exeter, United Kingdom
Paperback. Condition: Brand New. 2014 edition. 92 pages. 8.75x6.25x0.25 inches. In Stock.
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Language: English
Published by Springer-Nature New York Inc, 2024
ISBN 10: 3031669576 ISBN 13: 9783031669576
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Language: English
Published by Springer International Publishing AG, Cham, 2024
ISBN 10: 3031669576 ISBN 13: 9783031669576
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Paperback. Condition: new. Paperback. This two-volume set LNCS 14859-14860 constitutes the proceedings of the 28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024, held in Manchester, UK, during July 2426, 2024.The 59 full papers included in this book were carefully reviewed and selected from 93 submissions. They were organized in topical sections as follows: Part I : Advancement in Brain Imaging; Medical Images and Computational Models; and Digital Pathology, Histology and Microscopic Imaging.Part II : Dental and Bone Imaging; Enhancing Low-Quality Medical Images; Domain Adaptation and Generalisation; and Dermatology, Cardiac Imaging and Other Medical Imaging. This two-volume set LNCS 14859-14860 constitutes the proceedings of the 28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024, held in Manchester, UK, during July 2426, 2024.The 59 full papers included in this book were carefully reviewed and selected from 93 submissions. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Language: English
Published by Springer-Verlag New York Inc, 2018
ISBN 10: 3319959204 ISBN 13: 9783319959207
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Language: English
Published by Springer International Publishing, 2014
ISBN 10: 3319039423 ISBN 13: 9783319039428
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The last few years have seen a great increase in the amount of data available to scientists, yet many of the techniques used to analyse this data cannot cope with such large datasets. Therefore, strategies need to be employed as a pre-processing step to reduce the number of objects or measurements whilst retaining important information. Spectral dimensionality reduction is one such tool for the data processing pipeline. Numerous algorithms and improvements have been proposed for the purpose of performing spectral dimensionality reduction, yet there is still no gold standard technique. This book provides a survey and reference aimed at advanced undergraduate and postgraduate students as well as researchers, scientists, and engineers in a wide range of disciplines. Dimensionality reduction has proven useful in a wide range of problem domains and so this book will be applicable to anyone with a solid grounding in statistics and computer science seeking to apply spectral dimensionality to their work.