Sprache: Englisch
Verlag: Academic Press (edition 2), 2020
ISBN 10: 0128156309 ISBN 13: 9780128156308
Anbieter: BooksRun, Philadelphia, PA, USA
Paperback. Zustand: Very Good. 2. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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In den WarenkorbPaperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
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In den WarenkorbZustand: New. In.
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In den WarenkorbZustand: New. pp. 550.
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In den WarenkorbZustand: New. In English.
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In den WarenkorbHardcover. Zustand: Brand New. In Stock.
Sprache: Englisch
Verlag: Elsevier Science Publishing Co Inc, 2020
ISBN 10: 0128156309 ISBN 13: 9780128156308
Anbieter: moluna, Greven, Deutschland
EUR 125,31
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In den WarenkorbZustand: 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.
Sprache: Englisch
Verlag: Elsevier Science Publishing Co Inc, 2020
ISBN 10: 0128156309 ISBN 13: 9780128156308
Anbieter: Buchpark, Trebbin, Deutschland
Zustand: Gut. Zustand: Gut | Seiten: 612 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
Zustand: New. 2025. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.
Sprache: Englisch
Verlag: Elsevier Science Publishing Co Inc Sep 2020, 2020
ISBN 10: 0128156309 ISBN 13: 9780128156308
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: 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.
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Advanced Techniques in Optimization for Machine Learning and Imaging | Alessandro Benfenati (u. a.) | Taschenbuch | Springer INdAM Series | x | Englisch | 2025 | Springer | EAN 9789819767717 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: 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.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: 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.