Fast Compact Algorithms and Software for Spline Smoothing [electronic resource] / by Howard L. Weinert.
Tipo de material:
TextoSeries SpringerBriefs in Computer Science | SpringerBriefs in Computer ScienceEditor: New York, NY : Springer New York : Imprint: Springer, 2013Descripción: VIII, 45 p. 7 illus., 5 illus. in color. online resourceTipo de contenido: - text
- computer
- online resource
- 9781461454960
- SpringerLink (Online service)
- 519.5 23
- QA276-280
Contenidos:
Springer eBooksResumen: Fast Compact Algorithms and Software for Spline Smoothing investigates algorithmic alternatives for computing cubic smoothing splines when the amount of smoothing is determined automatically by minimizing the generalized cross-validation score. These algorithms are based on Cholesky factorization, QR factorization, or the fast Fourier transform. All algorithms are implemented in MATLAB and are compared based on speed, memory use, and accuracy. An overall best algorithm is identified, which allows very large data sets to be processed quickly on a personal computer.
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Introduction -- Cholesky Algorithm -- QR Algorithm -- FFT Algorithm -- Discrete Spline Smoothing.
Fast Compact Algorithms and Software for Spline Smoothing investigates algorithmic alternatives for computing cubic smoothing splines when the amount of smoothing is determined automatically by minimizing the generalized cross-validation score. These algorithms are based on Cholesky factorization, QR factorization, or the fast Fourier transform. All algorithms are implemented in MATLAB and are compared based on speed, memory use, and accuracy. An overall best algorithm is identified, which allows very large data sets to be processed quickly on a personal computer.
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