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Advances in Data Science / Ilke Demir ... [et al.] editors
| Advances in Data Science / Ilke Demir ... [et al.] editors |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | xx, 364 p. : ill. ; 24 cm |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
94-XX - Information and communication theory, circuits [MSC 2020] 00B25 - Proceedings of conferences of miscellaneous specific interest [MSC 2020] 68Txx - Artificial intelligence [MSC 2020] 62-XX - Statistics [MSC 2020] 62H35 - Image analysis in multivariate analysis [MSC 2020] 94A08 - Image processing (compression, reconstruction, etc.) in information and communication theory [MSC 2020] 62H30 - Classification and discrimination; cluster analysis (statistical aspects) [MSC 2020] |
| Soggetto non controllato |
Data analysis
Discrete approximations Health science Incomplete and multi-modal data Methods involving duality Networking Regularization Spatial-temporal dynamics modeling Statistical topological learning algorithms User anonymity |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0274518 |
| Cham, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Advances in Data Science / Ilke Demir ... [et al.] editors
| Advances in Data Science / Ilke Demir ... [et al.] editors |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | xx, 364 p. : ill. ; 24 cm |
| Soggetto topico |
00B25 - Proceedings of conferences of miscellaneous specific interest [MSC 2020]
62-XX - Statistics [MSC 2020] 62H30 - Classification and discrimination; cluster analysis (statistical aspects) [MSC 2020] 62H35 - Image analysis in multivariate analysis [MSC 2020] 68-XX - Computer science [MSC 2020] 68Txx - Artificial intelligence [MSC 2020] 94-XX - Information and communication theory, circuits [MSC 2020] 94A08 - Image processing (compression, reconstruction, etc.) in information and communication theory [MSC 2020] |
| Soggetto non controllato |
Data analysis
Discrete approximations Health science Incomplete and multi-modal data Methods involving duality Networking Regularization Spatial-temporal dynamics modeling Statistical topological learning algorithms User anonymity |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00274518 |
| Cham, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Computational methods for applied inverse problems [[electronic resource] /] / edited by Yanfei Wang, Anatoly G. Yagola, Changchun Yang
| Computational methods for applied inverse problems [[electronic resource] /] / edited by Yanfei Wang, Anatoly G. Yagola, Changchun Yang |
| Pubbl/distr/stampa | Berlin, : De Gruyter, 2012 |
| Descrizione fisica | 1 online resource (552 p.) |
| Disciplina |
515
515.35 515/.35 |
| Altri autori (Persone) |
WangYanfei <1973->
YagolaA. G YangChangchun <1945-> |
| Collana | Inverse and ill-posed problems series |
| Soggetto topico |
Inverse problems (Differential equations) - Numerical solutions
Differential equations - Numerical solutions |
| Soggetto non controllato |
Computational Method
Geography Geophysics Image Processing Inverse Problem Oceanography Operator Theory Optimization Regularization Remote Sensing |
| ISBN |
1-283-85670-0
3-11-025905-2 |
| Classificazione | SK 920 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front matter -- Preface -- Editor's Preface -- Contents -- Part I. Introduction -- Chapter 1. Inverse Problems of Mathematical Physics / Kabanikhin, S. I. -- Part II. Recent Advances in Regularization Theory and Methods -- Chapter 2. Using Parallel Computing for Solving Multidimensional Ill-posed Problems / Lukyanenko, D. V. / Yagola, A. G. -- Chapter 3. Regularization of Fredholm Integral Equations of the First Kind using Nyström Approximation / Nair, M. T. -- Chapter 4. Regularization of Numerical Differentiation: Methods and Applications / Xiao, T. Y. / Zhang, H. / Hao, L. L. -- Chapter 5. Numerical Analytic Continuation and Regularization / Fu, C. L. / Cheng, H. / Ma, Y. J. -- Chapter 6. An Optimal Perturbation Regularization Algorithm for Function Reconstruction and Its Applications / Li, G. S. -- Chapter 7. Filtering and Inverse Problems Solving / Zotov, L. V. / Panteleev, V. L. -- Part III. Optimal Inverse Design and Optimization Methods -- Chapter 8. Inverse Design of Alloys' Chemistry for Specified Thermo-Mechanical Properties by using Multi-objective Optimization / Dulikravich, G. S. / Egorov, I. N. -- Chapter 9. Two Approaches to Reduce the Parameter Identification Errors / Xiang, Z. H. -- Chapter 10. A General Convergence Result for the BFGS Method / Dai, Y. H. -- Part IV. Recent Advances in Inverse Scattering -- Chapter 11. Uniqueness Results for Inverse Scattering Problems / Liu, X. D. / Zhang, B. -- Chapter 12. Shape Reconstruction of Inverse Medium Scattering for the Helmholtz Equation / Bao, G. / Li, P. J. -- Part V. Inverse Vibration, Data Processing and Imaging -- Chapter 13. Numerical Aspects of the Calculation of Molecular Force Fields from Experimental Data / Kuramshina, G. M. / Kochikov, I. V. / Stepanova, A. V. -- Chapter 14. Some Mathematical Problems in Biomedical Imaging / Liu, J. J. / Xu, H. L. -- Part VI Numerical Inversion in Geosciences -- Chapter 15. Numerical Methods for Solving Inverse Hyperbolic Problems / Kabanikhin, S. I. / Shishlenin, M. A. -- Chapter 16. Inversion Studies in Seismic Oceanography / Song, H. B. / Huang, X. H. / Pinheiro, L. M. / Song, Y. / Dong, C. Z. / Bai, Y. -- Chapter 17. Image Resolution Beyond the Classical Limit / Gelius, L. J. -- Chapter 18. Seismic Migration and Inversion / Wang, Y. F. / Li, Z. H. / Yang, C. C. -- Chapter 19. Seismic Wavefields Interpolation Based on Sparse Regularization and Compressive Sensing / Wang, Y. F. / Cao, J. J. / Sun, T. / Yang, C. C. -- Chapter 20. Some Researches on Quantitative Remote Sensing Inversion / Yang, H. -- Index |
| Record Nr. | UNINA-9910786433903321 |
| Berlin, : De Gruyter, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Computational Methods for Inverse Problems in Imaging / Marco Donatelli, Stefano Serra-Capizzano editors
| Computational Methods for Inverse Problems in Imaging / Marco Donatelli, Stefano Serra-Capizzano editors |
| Pubbl/distr/stampa | Cham, : Springer, 2019 |
| Descrizione fisica | ix, 166 p. : ill. ; 24 cm |
| Soggetto topico |
94-XX - Information and communication theory, circuits [MSC 2020]
65-XX - Numerical analysis [MSC 2020] |
| Soggetto non controllato |
Computational methods
Image reconstruction Inverse Problems Optimization methods Regularization |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0126791 |
| Cham, : Springer, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Computational Methods for Inverse Problems in Imaging / Marco Donatelli, Stefano Serra-Capizzano editors
| Computational Methods for Inverse Problems in Imaging / Marco Donatelli, Stefano Serra-Capizzano editors |
| Pubbl/distr/stampa | Cham, : Springer, 2019 |
| Descrizione fisica | ix, 166 p. : ill. ; 24 cm |
| Soggetto topico |
65-XX - Numerical analysis [MSC 2020]
94-XX - Information and communication theory, circuits [MSC 2020] |
| Soggetto non controllato |
Computational methods
Image reconstruction Inverse Problems Optimization methods Regularization |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00126791 |
| Cham, : Springer, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Computer modelling in tomography and ill-posed problems / / M.M. Lavrentèv, S.M. Zerkal and O.E. Trofimov
| Computer modelling in tomography and ill-posed problems / / M.M. Lavrentèv, S.M. Zerkal and O.E. Trofimov |
| Autore | Lavrentʹev M. M (Mikhail Mikhaĭlovich) |
| Edizione | [Reprint 2014] |
| Pubbl/distr/stampa | Utrecht ; ; Boston : , : VSP, , 2001 |
| Descrizione fisica | 1 online resource (136 pages) : illustrations |
| Disciplina | 516 |
| Collana | Inverse and ill-posed problems series |
| Soggetto topico |
Geometric tomography
Inverse problems (Differential equations) |
| Soggetto non controllato |
Algorithms
Calculation Mathematics Cone-beam Ill-posed Problems Inverse Kinematic Problem Regularization Tomography |
| ISBN | 3-11-094093-0 |
| Classificazione | ST 640 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Chapter 1. Mathematical basis of the method of computerized -- tomography 11 -- 1.1. Basic notions of the theory of ill-posed problems11 -- 1.2. Problem of integral geometry16 -- 1.3. The Radon transform18 -- 1.4. Radon problem as an example of an ill-posed problem20 -- 1.5. The algorithm of inversion of the two-dimensional Radon -- transform based on the convolution with the generalized -- function l/z225 -- Chapter 2. Cone-beam tomography reconstruction 33 -- 2.1. Reducing the inversion formulas of cone-beam tomography recont -- struction to the form convenient for constructing numerical -- algorithm s33 -- 2.2. Elements of the theory of generalized functions in application to -- problems of inversion of the ray transformation45 -- 2.3. The relations between the Radon, Fourier, -- and ray transformations51 -- Chapter 3. Inverse kinematic problem -- in the tomographic setting 55 -- 3.1. Direct kinematic problem and numerical solution -- for three-dimensional regular media55 -- 3.2. Formulation of the inverse kinematic problem with the use of -- a tomography system of data gathering66 -- 3.3. Deduction of the basic inversion formula and the algorithm of -- solving the inverse kinematic problem in -- three-dimensional linearized formulation68 -- 3.4. Model experiment and numerical study of the algorithm79 -- 3.5. Solution of the inverse kinematic problem by the method of -- computerized tomography for media with opaque inclusions 98 -- Appendix: Reconstruction with the use -- of the standard model 112 -- Bibliography 119. |
| Record Nr. | UNINA-9910788955503321 |
Lavrentʹev M. M (Mikhail Mikhaĭlovich)
|
||
| Utrecht ; ; Boston : , : VSP, , 2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Computer modelling in tomography and ill-posed problems / / M.M. Lavrentèv, S.M. Zerkal and O.E. Trofimov
| Computer modelling in tomography and ill-posed problems / / M.M. Lavrentèv, S.M. Zerkal and O.E. Trofimov |
| Autore | Lavrentʹev M. M (Mikhail Mikhaĭlovich) |
| Edizione | [Reprint 2014] |
| Pubbl/distr/stampa | Utrecht ; ; Boston : , : VSP, , 2001 |
| Descrizione fisica | 1 online resource (136 pages) : illustrations |
| Disciplina | 516 |
| Collana | Inverse and ill-posed problems series |
| Soggetto topico |
Geometric tomography
Inverse problems (Differential equations) |
| Soggetto non controllato |
Algorithms
Calculation Mathematics Cone-beam Ill-posed Problems Inverse Kinematic Problem Regularization Tomography |
| ISBN | 3-11-094093-0 |
| Classificazione | ST 640 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Chapter 1. Mathematical basis of the method of computerized -- tomography 11 -- 1.1. Basic notions of the theory of ill-posed problems11 -- 1.2. Problem of integral geometry16 -- 1.3. The Radon transform18 -- 1.4. Radon problem as an example of an ill-posed problem20 -- 1.5. The algorithm of inversion of the two-dimensional Radon -- transform based on the convolution with the generalized -- function l/z225 -- Chapter 2. Cone-beam tomography reconstruction 33 -- 2.1. Reducing the inversion formulas of cone-beam tomography recont -- struction to the form convenient for constructing numerical -- algorithm s33 -- 2.2. Elements of the theory of generalized functions in application to -- problems of inversion of the ray transformation45 -- 2.3. The relations between the Radon, Fourier, -- and ray transformations51 -- Chapter 3. Inverse kinematic problem -- in the tomographic setting 55 -- 3.1. Direct kinematic problem and numerical solution -- for three-dimensional regular media55 -- 3.2. Formulation of the inverse kinematic problem with the use of -- a tomography system of data gathering66 -- 3.3. Deduction of the basic inversion formula and the algorithm of -- solving the inverse kinematic problem in -- three-dimensional linearized formulation68 -- 3.4. Model experiment and numerical study of the algorithm79 -- 3.5. Solution of the inverse kinematic problem by the method of -- computerized tomography for media with opaque inclusions 98 -- Appendix: Reconstruction with the use -- of the standard model 112 -- Bibliography 119. |
| Record Nr. | UNINA-9910813739403321 |
|
Lavrentʹev M. M (Mikhail Mikhaĭlovich)
|
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| Utrecht ; ; Boston : , : VSP, , 2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Fundamentals of High-Dimensional Statistics : With Exercises and R Labs / Johannes Lederer
| Fundamentals of High-Dimensional Statistics : With Exercises and R Labs / Johannes Lederer |
| Autore | Lederer, Johannes |
| Pubbl/distr/stampa | Cham, : Springer, 2022 |
| Descrizione fisica | xiv, 355 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62H22 - Probabilistic graphical models [MSC 2020] 62J07 - Ridge regression; shrinkage estimators (Lasso) [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] |
| Soggetto non controllato |
Calibration
Estimation Graphical Models High dimensional inference High-Dimensional Data High-dimensional statistics Lasso Linear regression Prediction R labs Regularization Sparsity Statistical inference |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0277448 |
|
Lederer, Johannes
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| Cham, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Fundamentals of High-Dimensional Statistics : With Exercises and R Labs / Johannes Lederer
| Fundamentals of High-Dimensional Statistics : With Exercises and R Labs / Johannes Lederer |
| Autore | Lederer, Johannes |
| Pubbl/distr/stampa | Cham, : Springer, 2022 |
| Descrizione fisica | xiv, 355 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62H22 - Probabilistic graphical models [MSC 2020] 62J07 - Ridge regression; shrinkage estimators (Lasso) [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] |
| Soggetto non controllato |
Calibration
Estimation Graphical Models High dimensional inference High-Dimensional Data High-dimensional statistics Lasso Linear regression Prediction R labs Regularization Sparsity Statistical inference |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00277448 |
|
Lederer, Johannes
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||
| Cham, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Fundamentals of Supervised Machine Learning : With Applications in Python, R, and Stata / Giovanni Cerulli
| Fundamentals of Supervised Machine Learning : With Applications in Python, R, and Stata / Giovanni Cerulli |
| Autore | Cerulli, Giovanni |
| Pubbl/distr/stampa | Cham, : Springer, 2023 |
| Descrizione fisica | xxiv, 391 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
68-XX - Computer science [MSC 2020] 68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020] 68T07 - Artificial neural networks and deep learning [MSC 2020] |
| Soggetto non controllato |
Artificial Neural Networks
Classification Deep Learning Discriminant Analysis Machine Learnig Applications in Medicine and Epidemiology Machine Learning Applications in the Social Sciences Machine learning Model selection Nearest Neighbors Nonparametric Fitting Ontology of Machine Learning Python R software Regularization Sentiment analysis Statistical learning Statistics Supervised machine learning Support Vector Machines Trees |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00278813 |
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Cerulli, Giovanni
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| Cham, : Springer, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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