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Published by Elsevier Science Publishing Co Inc, 2020
ISBN 10: 0128156309 ISBN 13: 9780128156308
Language: English
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Add to basketCondition: acceptable. This copy has clearly been enjoyedâ"expect noticeable shelf wear and some minor creases to the cover. Binding is strong, and all pages are legible. May contain previous library markings or stamps.
Published by Elsevier Science Publishing Co Inc, 2020
ISBN 10: 0128156309 ISBN 13: 9780128156308
Language: English
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Published by Academic Press 2020-09-18, 2020
ISBN 10: 0128156309 ISBN 13: 9780128156308
Language: English
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Add to basketHardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Published by Elsevier Science Publishing Co Inc, 2020
ISBN 10: 0128156309 ISBN 13: 9780128156308
Language: English
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Add to basketCondition: New. Über den AutorrnrnRobert Kissell, Ph.D., is President of Kissell Research Group, a global financial and economic consulting firm specializing in quantitative modeling, statistical analysis, and algorithmic trading. He is also a professor at.
Published by Elsevier Science Publishing Co Inc Sep 2020, 2020
ISBN 10: 0128156309 ISBN 13: 9780128156308
Language: English
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Add to basketTaschenbuch. Condition: Neu. Neuware - Provides insights into all necessary components of algorithmic trading, including transaction costs analysis, market impact, risk and optimization, and a thorough and detailed discussion of trading algorithms Includes increased coverage of mathematics, statistics and machine learning Presents broad coverage of Alpha Model construction.
Published by Springer Nature Singapore, Springer Nature Singapore, 2024
ISBN 10: 9819767687 ISBN 13: 9789819767687
Language: English
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Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - In recent years, non-linear optimization has had a crucial role in the development of modern techniques at the interface of machine learning and imaging. The present book is a collection of recent contributions in the field of optimization, either revisiting consolidated ideas to provide formal theoretical guarantees or providing comparative numerical studies for challenging inverse problems in imaging. The work of these papers originated in the INdAM Workshop 'Advanced Techniques in Optimization for Machine learning and Imaging' held in Roma, Italy, on June 20-24, 2022.The covered topics include non-smooth optimisation techniques for model-driven variational regularization, fixed-point continuation algorithms and their theoretical analysis for selection strategies of the regularization parameter for linear inverse problems in imaging, different perspectives on Support Vector Machines trained via Majorization-Minimization methods, generalization of Bayesian statistical frameworks to imaging problems, and creation of benchmark datasets for testing new methods and algorithms.
Published by Springer Nature Singapore, Springer Nature Singapore Okt 2024, 2024
ISBN 10: 9819767687 ISBN 13: 9789819767687
Language: English
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Add to basketBuch. Condition: Neu. Neuware -In recent years, non-linear optimization has had a crucial role in the development of modern techniques at the interface of machine learning and imaging. The present book is a collection of recent contributions in the field of optimization, either revisiting consolidated ideas to provide formal theoretical guarantees or providing comparative numerical studies for challenging inverse problems in imaging. The work of these papers originated in the INdAM Workshop ¿Advanced Techniques in Optimization for Machine learning and Imaging¿ held in Roma, Italy, on June 20-24, 2022.The covered topics include non-smooth optimisation techniques for model-driven variational regularization, fixed-point continuation algorithms and their theoretical analysis for selection strategies of the regularization parameter for linear inverse problems in imaging, different perspectives on Support Vector Machines trained via Majorization-Minimization methods, generalization of Bayesian statistical frameworks to imaging problems, and creation of benchmark datasets for testing new methods and algorithms.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 176 pp. Englisch.
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Published by Elsevier Science Publishing Co Inc, 2020
ISBN 10: 0128156309 ISBN 13: 9780128156308
Language: English
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Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 1209.
Published by Springer, Berlin, Springer Nature Singapore, Springer, 2024
ISBN 10: 9819767687 ISBN 13: 9789819767687
Language: English
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Add to basketBuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In recent years, non-linear optimization has had a crucial role in the development of modern techniques at the interface of machine learning and imaging. The present book is a collection of recent contributions in the field of optimization, either revisiting consolidated ideas to provide formal theoretical guarantees or providing comparative numerical studies for challenging inverse problems in imaging. The work of these papers originated in the INdAM Workshop 'Advanced Techniques in Optimization for Machine learning and Imaging' held in Roma, Italy, on June 20-24, 2022.The covered topics include non-smooth optimisation techniques for model-driven variational regularization, fixed-point continuation algorithms and their theoretical analysis for selection strategies of the regularization parameter for linear inverse problems in imaging, different perspectives on Support Vector Machines trained via Majorization-Minimization methods, generalization of Bayesian statistical frameworks to imaging problems, and creation of benchmark datasets for testing new methods and algorithms. 165 pp. Englisch.
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