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Kernel-based Methods and Applications
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Kernel-based Methods and Applications
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Academic year 2020/2021
- Teachers
- Roberto Cavoretto
Alessandra De Rossi - Teaching period
- Apr-July
- Type
- Basic
- Credits/Recognition
- Durata: 30 hours [6 CFU]
- Course disciplinary sector (SSD)
- MAT/08 - numerical analysis
- Delivery
- Formal authority
- Language
- English
- Attendance
- Obligatory
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Sommario del corso
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Course objectives
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Program
• Scattered data interpolation.
• Kernel methods and radial basis functions.
• Partition of unity methods.
• Generation of scattered data sets on the plane and on the sphere.
• Spherical interpolation.
• Application of radial basis functions in image processing.
• Fast algorithms for interpolation of large scattered data sets.
• Numerical linear algebra methods in scattered data approximation.Suggested readings and bibliography
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• M. D. Buhmann, Radial Basis Functions: Theory and Implementation, Cambridge Monogr. Appl. Comput. Math., vol. 12, Cambridge Univ. Press, Cambridge, 2003.
• G. E. Fasshauer, Meshfree Approximation Methods with Matlab, World Scientific Publishers, Singapore, 2007.
• G. Fasshauer, M. McCourt, Kernel-based Approximation Methods using Matlab, World Scientific, Singapore, 2015.
• J. Modersitzki, Numerical Methods for Image Registration, Oxford Univ. Press, New York, 2004.
• K. Rohr, Landmark-Based Image Analysis, Using Geometric and Intensity Models, Kluwer Academic Publishers, Norwell, MA, 2001.
• H. Wendland, Scattered Data Approximation, Cambridge Monogr. Appl. Comput. Math., vol. 17, Cambridge Univ. Press, Cambridge, 2005.- Oggetto:
Notes
Availability to provide the course onlineLessons between: March - May 2021
First class: March 5th, 2021, at 10:00 am; webex link: https://unito.webex.com/meet/
alessandra.derossi - Oggetto:
