Relevant bibliographies by topics / Parameter of regularization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Parameter of regularization'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Parameter of regularization.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Parameter of regularization"

1

Ito, Kazufumi, Bangti Jin, and Tomoya Takeuchi. "A Regularization Parameter for Nonsmooth Tikhonov Regularization." SIAM Journal on Scientific Computing 33, no. 3 (2011): 1415–38. http://dx.doi.org/10.1137/100790756.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Hyötyniemi, Heikki. "Regularization of Parameter Estimation." IFAC Proceedings Volumes 29, no. 1 (1996): 4652–57. http://dx.doi.org/10.1016/s1474-6670(17)58416-5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Ito, Kazufumi, Bangti Jin, and Tomoya Takeuchi. "Multi-parameter Tikhonov regularization." Methods and Applications of Analysis 18, no. 1 (2011): 31–46. http://dx.doi.org/10.4310/maa.2011.v18.n1.a2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Xu, Xiaowei, and Ting Bu. "An Adaptive Parameter Choosing Approach for Regularization Model." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 08 (2018): 1859013. http://dx.doi.org/10.1142/s0218001418590139.

Full text
Abstract:
The choice of regularization parameters is a troublesome issue for most regularization methods, e.g. Tikhonov regularization method, total variation (TV) method, etc. An appropriate parameter for a certain regularization approach can obtain fascinating results. However, general methods of choosing parameters, e.g. Generalized Cross Validation (GCV), cannot get more precise results in practical applications. In this paper, we consider exploiting the more appropriate regularization parameter within a possible range, and apply the estimated parameter to Tikhonov model. In the meanwhile, we obtain
APA, Harvard, Vancouver, ISO, and other styles
5

Omer, Hammad, Mahmood Qureshi, and Robert J. Dickinson. "Regularization-based SENSE reconstruction and choice of regularization parameter." Concepts in Magnetic Resonance Part A 44, no. 2 (2015): 67–73. http://dx.doi.org/10.1002/cmr.a.21328.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Hanhela, Matti, Olli Gröhn, Mikko Kettunen, Kati Niinimäki, Marko Vauhkonen, and Ville Kolehmainen. "Data-Driven Regularization Parameter Selection in Dynamic MRI." Journal of Imaging 7, no. 2 (2021): 38. http://dx.doi.org/10.3390/jimaging7020038.

Full text
Abstract:
In dynamic MRI, sufficient temporal resolution can often only be obtained using imaging protocols which produce undersampled data for each image in the time series. This has led to the popularity of compressed sensing (CS) based reconstructions. One problem in CS approaches is determining the regularization parameters, which control the balance between data fidelity and regularization. We propose a data-driven approach for the total variation regularization parameter selection, where reconstructions yield expected sparsity levels in the regularization domains. The expected sparsity levels are
APA, Harvard, Vancouver, ISO, and other styles
7

Furukawa, Tomonari. "Parameter identification with weightless regularization." International Journal for Numerical Methods in Engineering 52, no. 3 (2001): 219–38. http://dx.doi.org/10.1002/nme.215.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Sanders, Toby. "Parameter selection for HOTV regularization." Applied Numerical Mathematics 125 (March 2018): 1–9. http://dx.doi.org/10.1016/j.apnum.2017.10.010.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Krawczyk-Stańdo, Dorota, and Marek Rudnicki. "Regularization Parameter Selection in Discrete Ill-Posed Problems — The Use of the U-Curve." International Journal of Applied Mathematics and Computer Science 17, no. 2 (2007): 157–64. http://dx.doi.org/10.2478/v10006-007-0014-3.

Full text
Abstract:
Regularization Parameter Selection in Discrete Ill-Posed Problems — The Use of the U-CurveTo obtain smooth solutions to ill-posed problems, the standard Tikhonov regularization method is most often used. For the practical choice of the regularization parameter α we can then employ the well-known L-curve criterion, based on the L-curve which is a plot of the norm of the regularized solution versus the norm of the corresponding residual for all valid regularization parameters. This paper proposes a new criterion for choosing the regularization parameter α, based on the so-called U-curve. A compa
APA, Harvard, Vancouver, ISO, and other styles
10

Zhu, Dixian, Changjie Cai, Tianbao Yang, and Xun Zhou. "A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization." Big Data and Cognitive Computing 2, no. 1 (2018): 5. http://dx.doi.org/10.3390/bdcc2010005.

Full text
Abstract:
In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., ozone, particle matter ( PM 2.5 ) and sulfur dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exist some works applying machine learning to air quality prediction, most of the prior studies are restricted to several-year data and simply train standard regression models (linear or nonlinear) to predict the hourly air polluti
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Parameter of regularization"

1

Rydström, Sara. "Regularization of Parameter Problems for Dynamic Beam Models." Licentiate thesis, Växjö University, School of Mathematics and Systems Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-7367.

Full text
Abstract:
<p>The field of inverse problems is an area in applied mathematics that is of great importance in several scientific and industrial applications. Since an inverse problem is typically founded on non-linear and ill-posed models it is a very difficult problem to solve. To find a regularized solution it is crucial to have <em>a priori</em> information about the solution. Therefore, general theories are not sufficient considering new applications.</p><p>In this thesis we consider the inverse problem to determine the beam bending stiffness from measurements of the transverse dynamic displacement. O
APA, Harvard, Vancouver, ISO, and other styles
2

Kayhan, Belgin. "Parameter Estimation In Generalized Partial Linear Modelswith Tikhanov Regularization." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612530/index.pdf.

Full text
Abstract:
Regression analysis refers to techniques for modeling and analyzing several variables in statistical learning. There are various types of regression models. In our study, we analyzed Generalized Partial Linear Models (GPLMs), which decomposes input variables into two sets, and additively combines classical linear models with nonlinear model part. By separating linear models from nonlinear ones, an inverse problem method Tikhonov regularization was applied for the nonlinear submodels separately, within the entire GPLM. Such a particular representation of submodels provides both a better accurac
APA, Harvard, Vancouver, ISO, and other styles
3

Hofmann, Bernd, and Peter Mathé. "Parameter choice in Banach space regularization under variational inequalities." Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-86241.

Full text
Abstract:
The authors study parameter choice strategies for Tikhonov regularization of nonlinear ill-posed problems in Banach spaces. The effectiveness of any parameter choice for obtaining convergence rates depend on the interplay of the solution smoothness and the nonlinearity structure, and it can be expressed concisely in terms of variational inequalities. Such inequalities are link conditions between the penalty term, the norm misfit and the corresponding error measure. The parameter choices under consideration include an a priori choice, the discrepancy principle as well as the Lepskii principle.
APA, Harvard, Vancouver, ISO, and other styles
4

Palmberger, Anna. "Regularization parameter selection methods for an inverse dispersion problem." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184296.

Full text
Abstract:
There are many regularization parameter selection methods that can be used when solving inverse problems, but it is not clear which one is best suited for the inverse dispersion problem. The suitability of three different methods for solving the inverse dispersion problem are evaluated here in order to pick a suitable method for these kinds of problems in the future. The regularization parameter selection methods are used to solve the separable non-linear inverse dispersion problem which is adjusted and solved as a linear inverse problem. It is solved with Tikhonov regularization and the model
APA, Harvard, Vancouver, ISO, and other styles
5

Goldes, John. "REGULARIZATION PARAMETER SELECTION METHODS FOR ILL POSED POISSON IMAGING PROBLEMS." The University of Montana, 2010. http://etd.lib.umt.edu/theses/available/etd-07072010-124233/.

Full text
Abstract:
A common problem in imaging science is to estimate some underlying true image given noisy measurements of image intensity. When image intensity is measured by the counting of incident photons emitted by the object of interest, the data-noise is accurately modeled by a Poisson distribution, which motivates the use of Poisson maximum likelihood estimation. When the underlying model equation is ill-posed, regularization must be employed. I will present a computational framework for solving such problems, including statistically motivated methods for choosing the regularization parameter. Numerica
APA, Harvard, Vancouver, ISO, and other styles
6

Anzengruber, Stephan W., Bernd Hofmann, and Peter Mathé. "Regularization properties of the discrepancy principle for Tikhonov regularization in Banach spaces." Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-99353.

Full text
Abstract:
The stable solution of ill-posed non-linear operator equations in Banach space requires regularization. One important approach is based on Tikhonov regularization, in which case a one-parameter family of regularized solutions is obtained. It is crucial to choose the parameter appropriately. Here, a variant of the discrepancy principle is analyzed. In many cases such parameter choice exhibits the feature, called regularization property below, that the chosen parameter tends to zero as the noise tends to zero, but slower than the noise level. Here we shall show such regularization property under
APA, Harvard, Vancouver, ISO, and other styles
7

Maaß, Peter, Sergei V. Pereverzev, Ronny Ramlau, and Sergei G. Solodky. "An adaptive discretization for Tikhonov-Phillips regularization with a posteriori parameter selection." Universität Potsdam, 1998. http://opus.kobv.de/ubp/volltexte/2007/1473/.

Full text
Abstract:
The aim of this paper is to describe an efficient strategy for descritizing ill-posed linear operator equations of the first kind: we consider Tikhonov-Phillips-regularization χ^δ α = (a * a + α I)^-1 A * y ^δ with a finite dimensional approximation A n instead of A. We propose a sparse matrix structure which still leads to optimal convergences rates but requires substantially less scalar products for computing A n compared with standard methods.
APA, Harvard, Vancouver, ISO, and other styles
8

Do, Thi Bich Tram [Verfasser], and Christian [Akademischer Betreuer] Clason. "Discrete regularization for parameter identification problems / Thi Bich Tram Do ; Betreuer: Christian Clason." Duisburg, 2019. http://d-nb.info/1193591112/34.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Rojas, Gómez Renán Alfredo. "Automatic regularization parameter selection for the total variation mixed noise image restoration framework." Master's thesis, Pontificia Universidad Católica del Perú, 2012. http://tesis.pucp.edu.pe/repositorio/handle/123456789/4461.

Full text
Abstract:
Image restoration consists in recovering a high quality image estimate based only on observations. This is considered an ill-posed inverse problem, which implies non-unique unstable solutions. Regularization methods allow the introduction of constraints in such problems and assure a stable and unique solution. One of these methods is Total Variation, which has been broadly applied in signal processing tasks such as image denoising, image deconvolution, and image inpainting for multiple noise scenarios. Total Variation features a regularization parameter which defines the solution regularizatio
APA, Harvard, Vancouver, ISO, and other styles
10

Park, Yonggi. "PARAMETER SELECTION RULES FOR ILL-POSED PROBLEMS." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1574079328985475.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Parameter of regularization"

1

Regularization methods in Banach spaces. De Gruyter, 2012.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Costas, Kravaris, Steinfeld John H, and Institute for Computer Applications in Science and Engineering., eds. History matching by spline approximation and regularization in single-phase areal reservoirs. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1986.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Cardot, Hervé, and Pascal Sarda. Functional Linear Regression. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.2.

Full text
Abstract:
This article presents a selected bibliography on functional linear regression (FLR) and highlights the key contributions from both applied and theoretical points of view. It first defines FLR in the case of a scalar response and shows how its modelization can also be extended to the case of a functional response. It then considers two kinds of estimation procedures for this slope parameter: projection-based estimators in which regularization is performed through dimension reduction, such as functional principal component regression, and penalized least squares estimators that take into account
APA, Harvard, Vancouver, ISO, and other styles
4

Chance, Kelly, and Randall V. Martin. Data Fitting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199662104.003.0011.

Full text
Abstract:
This chapter explores several of the most common and useful approaches to atmospheric data fitting as well as the process of using air mass factors to produce vertical atmospheric column abundances from line-of-sight slant columns determined by data fitting. An atmospheric spectrum or other type of atmospheric sounding is usually fitted to a parameterized physical model by minimizing a cost function, usually chi-squared. Linear fitting, when the model of the measurements is linear in the model parameters is described, followed by the more common nonlinear fitting case. For nonlinear fitting, t
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Parameter of regularization"

1

Sun, Ne-Zheng, and Alexander Sun. "Multiobjective Inversion and Regularization." In Model Calibration and Parameter Estimation. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2323-6_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Abidi, Mongi A., Andrei V. Gribok, and Joonki Paik. "Selection of the Regularization Parameter." In Advances in Computer Vision and Pattern Recognition. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46364-3_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Manhart, Michael, Andreas Maier, Joachim Hornegger, and Arnd Doerfler. "Fast Adaptive Regularization for Perfusion Parameter Computation." In Informatik aktuell. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46224-9_54.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Titterington, D. M. "Choosing the regularization parameter in image restoration." In Institute of Mathematical Statistics Lecture Notes - Monograph Series. Institute of Mathematical Statistics, 1991. http://dx.doi.org/10.1214/lnms/1215460514.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Lu, Feng, Zhaoxia Yang, and Yuesheng Li. "Wavelets Approach in Choosing Adaptive Regularization Parameter." In Wavelet Analysis and Its Applications. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45333-4_52.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Candemir, Sema, and Yusuf Sinan Akgül. "Adaptive Regularization Parameter for Graph Cut Segmentation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13772-3_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Furukawa, Tomonari, Chen Jian Ken Lee, and John G. Michopoulos. "Regularization for Parameter Identification Using Multi-Objective Optimization." In Multi-Objective Machine Learning. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-33019-4_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Lucchese, Mirko, Iuri Frosio, and N. Alberto Borghese. "Optimal Choice of Regularization Parameter in Image Denoising." In Image Analysis and Processing – ICIAP 2011. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24085-0_55.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Zhu, Lin. "Optimal Regularization Parameter Estimation for Regularized Discriminant Analysis." In Advanced Intelligent Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24728-6_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Cai, Jianqing, Erik W. Grafarend, and Burkhard Schaffrin. "The A-optimal regularization parameter in uniform Tykhonov-Phillips regularization — α-weighted BLE-." In International Association of Geodesy Symposia. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-10735-5_41.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Parameter of regularization"

1

Luiken, N., and T. van Leeuwen. "Estimating the Regularization Parameter Efficiently." In 80th EAGE Conference and Exhibition 2018. EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201801348.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Chen, Wen, Xiangzhong Fang, and Yan Cheng. "Super resolution with simultaneous determination of registration parameters and regularization parameter." In 2011 IEEE International Conference on Computer Science and Automation Engineering (CSAE). IEEE, 2011. http://dx.doi.org/10.1109/csae.2011.5952743.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Song-Na Guo, Xuan Yang, and Hong-Yuan Sun. "Optimal regularization parameter in approximate TPS interpolation." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620614.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Kouw, Wouter M., and Marco Loog. "On regularization parameter estimation under covariate shift." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7899671.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Wang, Yun, Zhen James Xiang, and Peter J. Ramadge. "Lasso screening with a small regularization parameter." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638277.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Zhang, Bin, and Fei Jin. "Parameter selections for Tikhonov regularization image restoration." In 2013 9th International Conference on Natural Computation (ICNC). IEEE, 2013. http://dx.doi.org/10.1109/icnc.2013.6818202.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Becker, Cassiano O., and Paulo A. V. Ferreira. "Gradient Hyper-parameter Optimization for Manifold Regularization." In 2013 12th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2013. http://dx.doi.org/10.1109/icmla.2013.145.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Bockmann, C., and S. Samaras. "Regularization Methods for Microphysical Aerosol Parameter Inversion." In Annual International Conference on Computational Mathematics, Computational Geometry & Statistics. Global Science and Technology Forum (GSTF), 2015. http://dx.doi.org/10.5176/2251-1911_cmcgs15.09.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Jeong, Jae Jin, Gyogwon Koo, Seung Hun Kim, and Sang Woo Kim. "Regularization parameter of normalized subband adaptive filter." In 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE). IEEE, 2014. http://dx.doi.org/10.1109/isie.2014.6864757.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Panahi, Ashkan, and Mats Viberg. "Maximum a posteriori based regularization parameter selection." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946980.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Parameter of regularization"

1

Horowitz, Joel L. Adaptive nonparametric instrumental variables estimation: empirical choice of the regularization parameter. Cemmap, 2013. http://dx.doi.org/10.1920/wp.cem.2013.3013.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Parameter of regularization' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography