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XII, 380 p. Softcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Advances in Intelligent Systems and Computing. Volume 705. Sprache: Englisch.
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In den WarenkorbZustand: New. Dr Bjorn Schuller Imperial College London & University of Augsburg CSO, audEERING 0000-0002-6478-8699Dr. Rajiv Gupta North Carolina State University, Raleigh, NC, United States 0000-0003-2684-1994Dr. Rakesh Mote Indian Institute of Tech.
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In den WarenkorbZustand: New. Rajiv Gupta North Carolina State University, Raleigh, NC, United StatesDevendra Deshmukh Indian Institute of Technology, IndoreAwanikumar P. Patil Visvesvaraya National Institute of Technology, NagpurNaveen Kumar .
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Taschenbuch. Zustand: Neu. Recent Advances in Material, Manufacturing, and Machine Learning | Proceedings of 1st International Conference (RAMMML-22), Volume 1 | Rajiv Gupta (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2023 | CRC Press | EAN 9781032416311 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Taschenbuch. Zustand: Neu. Neuware - These proceedings bring together both industrial and academic professionals to exchange and share their experiences, most recent innovations, trends and research results on all aspects of Advances in Materials, Manufacturing and Machine Learning.
Sprache: Englisch
Verlag: Taylor & Francis Ltd Jun 2024, 2024
ISBN 10: 1032584793 ISBN 13: 9781032584799
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware - The main aim of the 2nd international conference on recent advances in materials manufacturing and machine learning processes-2023 (RAMMML-23) is to bring together all interested academic researchers, scientists, engineers, and technocrats.
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Taschenbuch. Zustand: Neu. Recent Advances in Robot Learning | Machine Learning | Judy A. Franklin (u. a.) | Taschenbuch | iv | Englisch | 2011 | Springer | EAN 9781461380641 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Zustand: Neu. Recent Advances in Logo Detection Using Machine Learning Paradigms | Theory and Practice | Yen-Wei Chen (u. a.) | Taschenbuch | Intelligent Systems Reference Library | xii | Englisch | 2025 | Springer | EAN 9783031598135 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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In den WarenkorbGebunden. Zustand: New. Recent Advances in Robot Learning contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning .
Taschenbuch. Zustand: Neu. Recent Advances in Internet of Things and Machine Learning | Real-World Applications | Valentina E. Balas (u. a.) | Taschenbuch | xxiv | Englisch | 2023 | Springer | EAN 9783030901219 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Unsupervised domain adaptation (UDA) is a challenging problem in machine learning where the model is trained on a source domain with labeled data and tested on a target domain with unlabeled data. In recent years, UDA has received significant attention from the research community due to its applicability in various real-world scenarios. This book provides a comprehensive review of state-of-the-art UDA methods and explores new variants of UDA that have the potential to advance the field.The book begins with a clear introduction to the UDA problem and is mainly organized into four technical sections, each focused on a specific piece of UDA research. The first section covers criterion optimization-based UDA, which aims to learn domain-invariant representations by minimizing the discrepancy between source and target domains. The second section discusses bi-classifier adversarial learning-based UDA, which creatively leverages adversarial learning by conducting a minimax game between the feature extractor and two task classifiers. The third section introduces source-free UDA, a novel UDA setting that does not require any raw data from the source domain. The fourth section presents active learning for UDA, which combines domain adaptation and active learning to reduce the amount of labeled data needed for adaptation.This book is suitable for researchers, graduate students, and practitioners who are interested in UDA and its applications in various fields, primarily in computer vision. The chapters are authored by leading experts in the field and provide a comprehensive and in-depth analysis of the current UDA methods and new directions for future research. With its broad coverage and cutting-edge research, this book is a valuable resource for anyone looking to advance their knowledge of UDA.
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering.
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Recent Advances in Robot Learning contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning methods, ranging from inductive logic programming to reinforcement learning, is being applied to many subproblems in robot perception and control, often with objectives as diverse as parameter calibration and concept formulation. While no unified robot learning framework has yet emerged to cover the variety of problems and approaches described in these papers and other publications, a clear set of shared issues underlies many robot learning problems. Machine learning, when applied to robotics, is situated: it is embedded into a real-world system that tightly integrates perception, decision making and execution. Since robot learning involves decision making, there is an inherent active learning issue. Robotic domains are usually complex, yet the expense of using actual robotic hardware often prohibits the collection of large amounts of training data. Most robotic systems are real-time systems. Decisions must be made within critical or practical time constraints. These characteristics present challenges and constraints to the learning system. Since these characteristics are shared by other important real-world application domains, robotics is a highly attractive area for research on machine learning. On the other hand, machine learning is also highly attractive to robotics. There is a great variety of open problems in robotics that defy a static, hand-coded solution. Recent Advances in Robot Learning is an edited volume of peer-reviewed original research comprising seven invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 23, Numbers 2 and 3).
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In den WarenkorbPaperback. Zustand: Brand New. reprint edition. 224 pages. 9.13x6.10x0.59 inches. In Stock.
Sprache: Englisch
Verlag: Springer International Publishing, Springer Nature Switzerland, 2024
ISBN 10: 3031598105 ISBN 13: 9783031598104
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents the current trends in deep learning-based object detection framework with a focus on logo detection tasks. It introduces a variety of approaches, including attention mechanisms and domain adaptation for logo detection, and describes recent advancement in object detection frameworks using deep learning. We offer solutions to the major problems such as the lack of training data and the domain-shift issues.This book provides numerous ways that deep learners can use for logo recognition, including:Deep learning-based end-to-end trainable architecture for logo detectionWeakly supervised logo recognition approach using attention mechanismsAnchor-free logo detection framework combining attention mechanisms to precisely locate logos in the real-world imagesUnsupervised logo detection that takes into account domain-shift issues from synthetic to real-world imagesApproach for logo detection modeling domain adaption task in the context of weakly supervised learning to overcome the lack of object-level annotation problem.The merit of our logo recognition technique is demonstrated using experiments, performance evaluation, and feature distribution analysis utilizing different deep learning frameworks.The book is directed to professors, researchers, practitioners in the field of engineering, computer science, and related fields as well as anyone interested in using deep learning techniques and applications in logo and various object detection tasks.
Sprache: Englisch
Verlag: Springer International Publishing, 2023
ISBN 10: 3030901211 ISBN 13: 9783030901219
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers a domain that is significantly impacted by the growth of soft computing. Internet of Things (IoT)-related applications are gaining much attention with more and more devices which are getting connected, and they become the potential components of some smart applications. Thus, a global enthusiasm has sparked over various domains such as health, agriculture, energy, security, and retail. So, in this book, the main objective is to capture this multifaceted nature of IoT and machine learning in one single place. According to the contribution of each chapter, the book also provides a future direction for IoT and machine learning research. The objectives of this book are to identify different issues, suggest feasible solutions to those identified issues, and enable researchers and practitioners from both academia and industry to interact with each other regarding emerging technologies related to IoT and machine learning. In this book, we look for novel chapters that recommend new methodologies, recent advancement, system architectures, and other solutions to prevail over the limitations of IoT and machine learning.