Auswahl der wissenschaftlichen Literatur zum Thema „Regularized approaches“
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Zeitschriftenartikel zum Thema "Regularized approaches"
G.V., Suresh, und Srinivasa Reddy E.V. „Uncertain Data Analysis with Regularized XGBoost“. Webology 19, Nr. 1 (20.01.2022): 3722–40. http://dx.doi.org/10.14704/web/v19i1/web19245.
Der volle Inhalt der QuelleTaniguchi, Michiaki, und Volker Tresp. „Averaging Regularized Estimators“. Neural Computation 9, Nr. 5 (01.07.1997): 1163–78. http://dx.doi.org/10.1162/neco.1997.9.5.1163.
Der volle Inhalt der QuelleLuft, Daniel, und Volker Schulz. „Simultaneous shape and mesh quality optimization using pre-shape calculus“. Control and Cybernetics 50, Nr. 4 (01.12.2021): 473–520. http://dx.doi.org/10.2478/candc-2021-0028.
Der volle Inhalt der QuelleEbadat, Afrooz, Giulio Bottegal, Damiano Varagnolo, Bo Wahlberg und Karl H. Johansson. „Regularized Deconvolution-Based Approaches for Estimating Room Occupancies“. IEEE Transactions on Automation Science and Engineering 12, Nr. 4 (Oktober 2015): 1157–68. http://dx.doi.org/10.1109/tase.2015.2471305.
Der volle Inhalt der QuelleFeng, Hesen, Lihong Ma und Jing Tian. „A Dynamic Convolution Kernel Generation Method Based on Regularized Pattern for Image Super-Resolution“. Sensors 22, Nr. 11 (01.06.2022): 4231. http://dx.doi.org/10.3390/s22114231.
Der volle Inhalt der QuelleRobitzsch, Alexander. „Implementation Aspects in Regularized Structural Equation Models“. Algorithms 16, Nr. 9 (18.09.2023): 446. http://dx.doi.org/10.3390/a16090446.
Der volle Inhalt der QuelleRobitzsch, Alexander. „Comparing Robust Linking and Regularized Estimation for Linking Two Groups in the 1PL and 2PL Models in the Presence of Sparse Uniform Differential Item Functioning“. Stats 6, Nr. 1 (25.01.2023): 192–208. http://dx.doi.org/10.3390/stats6010012.
Der volle Inhalt der QuelleLeen, Todd K. „From Data Distributions to Regularization in Invariant Learning“. Neural Computation 7, Nr. 5 (September 1995): 974–81. http://dx.doi.org/10.1162/neco.1995.7.5.974.
Der volle Inhalt der QuelleFeng, Huijie, Chunpeng Wu, Guoyang Chen, Weifeng Zhang und Yang Ning. „Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 04 (03.04.2020): 3858–65. http://dx.doi.org/10.1609/aaai.v34i04.5798.
Der volle Inhalt der QuelleZhang, Hong, Dong Lai Hao und Xiang Yang Liu. „A Precoding Strategy for Massive MIMO System“. Applied Mechanics and Materials 568-570 (Juni 2014): 1278–81. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.1278.
Der volle Inhalt der QuelleDissertationen zum Thema "Regularized approaches"
Schwarz, Stephan [Verfasser], Philipp [Gutachter] Junker und Klaus [Gutachter] Hackl. „Efficient approaches for regularized damage models : variational modeling and numerical treatment / Stephan Schwarz ; Gutachter: Philipp Junker, Klaus Hackl ; Fakultät für Maschinenbau“. Bochum : Ruhr-Universität Bochum, 2019. http://d-nb.info/1195220863/34.
Der volle Inhalt der QuelleSpagnoli, Lorenzo. „COVID-19 prognosis estimation from CAT scan radiomics: comparison of different machine learning approaches for predicting patients survival and ICU Admission“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23926/.
Der volle Inhalt der QuelleSavino, Mary Edith. „Statistical learning methods for nonlinear geochemical problems“. Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASM032.
Der volle Inhalt der QuelleIn this thesis, we propose two function estimation methods and a variable selection method in a multivariate nonparametric model as part of numerical simulations of geochemical systems, for a deep geological disposal facility of highly radioactive waste. More specifically, in Chapter 2, we present an active learning procedure using Gaussian processes to approximate unknown functions having several input variables. This method allows for the computation of the global uncertainty of the function estimation at each iteration and thus, cunningly selects the most relevant observation points at which the function to estimate has to be evaluated. Consequently, the number of observations needed to obtain a satisfactory estimation of the underlying function is reduced, limiting calls to geochemical reaction equations solvers and reducing calculation times. Additionally, in Chapter 3, we propose a non sequential function estimation method called GLOBER consisting in approximating the function to estimate by a linear combination of B-splines. In this approach, since the knots of the B-splines can be seen as changes in the derivatives of the function to estimate, they are selected using the generalized lasso. In Chapter 4, we introduce a novel variable selection method in a multivariate nonparametric model, ABSORBER, to identify the variables the unknown function really depends on, thereby simplifying the geochemical system. In this approach, we assume that the function can be approximated by a linear combination of B-splines and their pairwise interaction terms. The coefficients of each term of the linear combination are estimated using the usual least squares criterion penalized by the l2-norms of the partial derivatives with respect to each variable. The introduced approaches were evaluated and validated through numerical experiments and were all applied to geochemical systems of varying complexity. Comparisons with state-of-the-art methods demonstrated that our methods outperformed the others. In Chapter 5, the function estimation and variable selection methods were applied in the context of a European project, EURAD, and compared to methods devised by other scientific teams involved in the projet. This application highlighted the performance of our methods, particularly when only the relevant variables selected with ABSORBER were considered. The proposed methods have been implemented in R packages: glober and absorber which are available on the CRAN (Comprehensive R Archive Network)
Mak, Rachel Y. C. „Reducing Complexity| A Regularized Non-negative Matrix Approximation (NNMA) Approach to X-ray Spectromicroscopy Analysis“. Thesis, Northwestern University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3669280.
Der volle Inhalt der QuelleX-ray absorption spectromicroscopy combines microscopy and spectroscopy to provide rich information about the chemical organization of materials down to the nanoscale. But with richness also comes complexity: natural materials such as biological or environmental science specimens can be composed of complex spectroscopic mixtures of different materials. The challenge becomes how we could meaningfully simplify and interpret this information. Approaches such as principal component analysis and cluster analysis have been used in previous studies, but with some limitations that we will describe. This leads us to develop a new approach based on a development of non-negative matrix approximation (NNMA) analysis with both sparseness and spectra similarity regularizations. We apply this new technique to simulated spectromicroscopy datasets as well as a preliminary study of the large-scale biochemical organization of a human sperm cell. NNMA analysis is able to select major features of the sperm cell without the physically erroneous negative weightings or thicknesses in the calculated image which appeared in previous approaches.
Yu, Lixi. „Regularized efficient score estimation and testing (reset) approach in low-dimensional and high-dimensional GLM“. Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2301.
Der volle Inhalt der QuelleGürol, Selime. „Solving regularized nonlinear least-squares problem in dual space with application to variational data assimilation“. Thesis, Toulouse, INPT, 2013. http://www.theses.fr/2013INPT0040/document.
Der volle Inhalt der QuelleThis thesis investigates the conjugate-gradient method and the Lanczos method for the solution of under-determined nonlinear least-squares problems regularized by a quadratic penalty term. Such problems often result from a maximum likelihood approach, and involve a set of m physical observations and n unknowns that are estimated by nonlinear regression. We suppose here that n is large compared to m. These problems are encountered for instance when three-dimensional fields are estimated from physical observations, as is the case in data assimilation in Earth system models. A widely used algorithm in this context is the Gauss-Newton (GN) method, known in the data assimilation community under the name of incremental four dimensional variational data assimilation. The GN method relies on the approximate solution of a sequence of linear least-squares problems in which the nonlinear least-squares cost function is approximated by a quadratic function in the neighbourhood of the current nonlinear iterate. However, it is well known that this simple variant of the Gauss-Newton algorithm does not ensure a monotonic decrease of the cost function and that convergence is not guaranteed. Removing this difficulty is typically achieved by using a line-search (Dennis and Schnabel, 1983) or trust-region (Conn, Gould and Toint, 2000) strategy, which ensures global convergence to first order critical points under mild assumptions. We consider the second of these approaches in this thesis. Moreover, taking into consideration the large-scale nature of the problem, we propose here to use a particular trust-region algorithm relying on the Steihaug-Toint truncated conjugate-gradient method for the approximate solution of the subproblem (Conn, Gould and Toint, 2000, pp. 133-139). Solving this subproblem in the n-dimensional space (by CG or Lanczos) is referred to as the primal approach. Alternatively, a significant reduction in the computational cost is possible by rewriting the quadratic approximation in the m-dimensional space associated with the observations. This is important for large-scale applications such as those solved daily in weather prediction systems. This approach, which performs the minimization in the m-dimensional space using CG or variants thereof, is referred to as the dual approach. The first proposed dual approach (Courtier, 1997), known as the Physical-space Statistical Analysis System (PSAS) in the data assimilation community starts by solving the corresponding dual cost function in m-dimensional space by a standard preconditioned CG (PCG), and then recovers the step in n-dimensional space through multiplication by an n by m matrix. Technically, the algorithm consists of recurrence formulas involving m-vectors instead of n-vectors. However, the use of PSAS can be unduly costly as it was noticed that the linear least-squares cost function does not monotonically decrease along the nonlinear iterations when applying standard termination. Another dual approach has been proposed by Gratton and Tshimanga (2009) and is known as the Restricted Preconditioned Conjugate Gradient (RPCG) method. It generates the same iterates in exact arithmetic as those generated by the primal approach, again using recursion formula involving m-vectors. The main interest of RPCG is that it results in significant reduction of both memory and computational costs while maintaining the desired convergence property, in contrast with the PSAS algorithm. The relation between these two dual approaches and the question of deriving efficient preconditioners (Gratton, Sartenaer and Tshimanga, 2011), essential when large-scale problems are considered, was not addressed in Gratton and Tshimanga (2009)
Pröchtel, Patrick. „Anisotrope Schädigungsmodellierung von Beton mit adaptiver bruchenergetischer Regularisierung Anisotropic damage modeling of concrete regularized by means of the adaptive fracture energy approach /“. [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1224751435667-29771.
Der volle Inhalt der QuelleTESEI, CLAUDIA. „Nonlinear analysis of masonry and concrete structures under monotonic and cyclic loading: a regularized multidirectional d+/d− damage model“. Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2710141.
Der volle Inhalt der QuelleOlaya, Bucaro Orlando. „Exploring relevant features associated with measles nonvaccination using a machine learning approach“. Thesis, Stockholms universitet, Sociologiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-184577.
Der volle Inhalt der QuelleSalgado, Patarroyo Ivan Camilo. „Spatially Regularized Spherical Reconstruction: A Cross-Domain Filtering Approach for HARDI Signals“. Thesis, 2013. http://hdl.handle.net/10012/7847.
Der volle Inhalt der QuelleBuchteile zum Thema "Regularized approaches"
Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao und Lennart Ljung. „Regularization in Reproducing Kernel Hilbert Spaces“. In Regularized System Identification, 181–246. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_6.
Der volle Inhalt der QuellePillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao und Lennart Ljung. „Numerical Experiments and Real World Cases“. In Regularized System Identification, 343–69. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_9.
Der volle Inhalt der QuelleGraham, Lamar A. „Chapter 4. Derived verbs and future-conditional stem regularization in written Spanish in synchrony and diachrony“. In Innovative Approaches to Research in Hispanic Linguistics, 82–105. Amsterdam: John Benjamins Publishing Company, 2023. http://dx.doi.org/10.1075/ihll.38.04gra.
Der volle Inhalt der QuelleIto, Kazufumi, und Bangti Jin. „Regularized Linear Inversion with Randomized Singular Value Decomposition“. In Mathematical and Numerical Approaches for Multi-Wave Inverse Problems, 45–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48634-1_5.
Der volle Inhalt der QuelleLombardi, Michele, Federico Baldo, Andrea Borghesi und Michela Milano. „An Analysis of Regularized Approaches for Constrained Machine Learning“. In Trustworthy AI - Integrating Learning, Optimization and Reasoning, 112–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73959-1_11.
Der volle Inhalt der Quellede Campos Souza, Paulo Vitor, Augusto Junio Guimaraes, Vanessa Souza Araujo, Thiago Silva Rezende und Vinicius Jonathan Silva Araujo. „Using Fuzzy Neural Networks Regularized to Support Software for Predicting Autism in Adolescents on Mobile Devices“. In Smart Network Inspired Paradigm and Approaches in IoT Applications, 115–33. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8614-5_7.
Der volle Inhalt der QuelleSchulz, Volker H., und Kathrin Welker. „Shape Optimization for Variational Inequalities of Obstacle Type: Regularized and Unregularized Computational Approaches“. In International Series of Numerical Mathematics, 397–420. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79393-7_16.
Der volle Inhalt der QuellePillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao und Lennart Ljung. „Bias“. In Regularized System Identification, 1–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_1.
Der volle Inhalt der QuellePillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao und Lennart Ljung. „Bayesian Interpretation of Regularization“. In Regularized System Identification, 95–134. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_4.
Der volle Inhalt der QuelleLuo, Ruiyan, Alejandra D. Herrera-Reyes, Yena Kim, Susan Rogowski, Diana White und Alexandra Smirnova. „Estimation of Time-Dependent Transmission Rate for COVID-19 SVIRD Model Using Predictor–Corrector Algorithm“. In Mathematical Modeling for Women’s Health, 213–37. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58516-6_7.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Regularized approaches"
Safari, Habibollah, und Mona Bavarian. „Enhancing Polymer Reaction Engineering Through the Power of Machine Learning“. In Foundations of Computer-Aided Process Design, 367–72. Hamilton, Canada: PSE Press, 2024. http://dx.doi.org/10.69997/sct.157792.
Der volle Inhalt der QuelleBrault, Dylan, Thomas Olivier, Ferréol Soulez und Corinne Fournier. „Automation of Gram stain imaging with multispectral in-line holography“. In Digital Holography and Three-Dimensional Imaging, M3B.2. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/dh.2024.m3b.2.
Der volle Inhalt der QuelleBudillon, Alessandra, Loic Denis, Clement Rambour, Gilda Schirinzi und Florence Tupin. „Regularized SAR Tomography Approaches“. In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9323807.
Der volle Inhalt der QuelleXiao, Yichi, Zhe Li, Tianbao Yang und Lijun Zhang. „SVD-free Convex-Concave Approaches for Nuclear Norm Regularization“. In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/436.
Der volle Inhalt der QuelleMeshgi, Kourosh, Maryam Sadat Mirzaei und Satoshi Sekine. „Uncertainty Regularized Multi-Task Learning“. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.wassa-1.8.
Der volle Inhalt der QuelleZhang, Lefei, Qian Zhang, Bo Du, Jane You und Dacheng Tao. „Adaptive Manifold Regularized Matrix Factorization for Data Clustering“. In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/475.
Der volle Inhalt der QuelleRajput, Shayam Singh, Deepak Rai und K. V. Arya. „Robust Image watermarking using Tikhonov regularized image reconstruction technique“. In 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2024. http://dx.doi.org/10.1109/iatmsi60426.2024.10502853.
Der volle Inhalt der QuelleNarayan, Jyotindra, Hassène Gritli und Santosha K. Dwivedy. „Lower Limb Joint Torque Estimation via Bayesian Regularized Backpropagation Neural Networks“. In 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2024. http://dx.doi.org/10.1109/iatmsi60426.2024.10502709.
Der volle Inhalt der QuelleLi, Jianxin, Haoyi Zhou, Pengtao Xie und Yingchun Zhang. „Improving the Generalization Performance of Multi-class SVM via Angular Regularization“. In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/296.
Der volle Inhalt der QuelleTomboulides, A., S. M. Aithal, P. F. Fischer, E. Merzari und A. Obabko. „A Novel Variant of the K-ω URANS Model for Spectral Element Methods: Implementation, Verification, and Validation in Nek5000“. In ASME 2014 4th Joint US-European Fluids Engineering Division Summer Meeting collocated with the ASME 2014 12th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/fedsm2014-21926.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Regularized approaches"
Da Gama Torres, Haroldo. Environmental Implications of Peri-urban Sprawl and the Urbanization of Secondary Cities in Latin America. Inter-American Development Bank, März 2011. http://dx.doi.org/10.18235/0008841.
Der volle Inhalt der QuelleS.R. Hudson. A Regularized Approach for Solving Magnetic Differential Equations and a Revised Iterative Equilibrium Algorithm. Office of Scientific and Technical Information (OSTI), Oktober 2010. http://dx.doi.org/10.2172/990749.
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