advanced techniques optimization machine (10 Ergebnisse)

Zustand: New. pp. 550.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New.

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Zustand: Gut. Zustand: Gut | Seiten: 612 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Zustand: New. &Uumlber 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.

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.

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.

Buch. Zustand: 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.

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.