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Relevant bibliographies by topics / Regularized approaches

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Academic literature on the topic 'Regularized approaches'

Author: Grafiati

Published: 19 October 2024

Last updated: 31 July 2025

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Journal articles on the topic "Regularized approaches"

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G.V., Suresh, and Srinivasa Reddy E.V. "Uncertain Data Analysis with Regularized XGBoost." Webology 19, no. 1 (2022): 3722–40. http://dx.doi.org/10.14704/web/v19i1/web19245.

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Uncertainty is a ubiquitous element in available knowledge about the real world. Data sampling error, obsolete sources, network latency, and transmission error are all factors that contribute to the uncertainty. These kinds of uncertainty have to be handled cautiously, or else the classification results could be unreliable or even erroneous. There are numerous methodologies developed to comprehend and control uncertainty in data. There are many faces for uncertainty i.e., inconsistency, imprecision, ambiguity, incompleteness, vagueness, unpredictability, noise, and unreliability. Missing infor

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Alexos, Antonios, Ian Domingo, and Pierre Baldi. "Improving Deep Learning Speed and Performance Through Synaptic Neural Balance." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 15339–46. https://doi.org/10.1609/aaai.v39i15.33683.

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We present theory of synaptic neural balance and we show experimentally that synaptic neural balance can improve deep learning speed, and accuracy, even in data-scarce environments. Given an additive cost function (regularizer) of the synaptic weights, a neuron is said to be in balance if the total cost of its incoming weights is equal to the total cost of its outgoing weights. For large classes of networks, activation functions, and regularizers, neurons can be balanced fully or partially using scaling operations that do not change their functionality. Furthermore, these balancing operations

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Taniguchi, Michiaki, and Volker Tresp. "Averaging Regularized Estimators." Neural Computation 9, no. 5 (1997): 1163–78. http://dx.doi.org/10.1162/neco.1997.9.5.1163.

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We compare the performance of averaged regularized estimators. We show that the improvement in performance that can be achieved by averaging depends critically on the degree of regularization which is used in training the individual estimators. We compare four different averaging approaches: simple averaging, bagging, variance-based weighting, and variance-based bagging. In any of the averaging methods, the greatest degree of improvement—if compared to the individual estimators—is achieved if no or only a small degree of regularization is used. Here, variance-based weighting and variance-based

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Luft, Daniel, and Volker Schulz. "Simultaneous shape and mesh quality optimization using pre-shape calculus." Control and Cybernetics 50, no. 4 (2021): 473–520. http://dx.doi.org/10.2478/candc-2021-0028.

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Abstract Computational meshes arising from shape optimization routines commonly suffer from decrease of mesh quality or even destruction of the mesh. In this work, we provide an approach to regularize general shape optimization problems to increase both shape and volume mesh quality. For this, we employ pre-shape calculus as established in Luft and Schulz (2021). Existence of regularized solutions is guaranteed. Further, consistency of modified pre-shape gradient systems is established. We present pre-shape gradient system modifications, which permit simultaneous shape optimization with mesh q

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Ebadat, Afrooz, Giulio Bottegal, Damiano Varagnolo, Bo Wahlberg, and Karl H. Johansson. "Regularized Deconvolution-Based Approaches for Estimating Room Occupancies." IEEE Transactions on Automation Science and Engineering 12, no. 4 (2015): 1157–68. http://dx.doi.org/10.1109/tase.2015.2471305.

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Feng, Hesen, Lihong Ma, and Jing Tian. "A Dynamic Convolution Kernel Generation Method Based on Regularized Pattern for Image Super-Resolution." Sensors 22, no. 11 (2022): 4231. http://dx.doi.org/10.3390/s22114231.

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Image super-resolution aims to reconstruct a high-resolution image from its low-resolution counterparts. Conventional image super-resolution approaches share the same spatial convolution kernel for the whole image in the upscaling modules, which neglect the specificity of content information in different positions of the image. In view of this, this paper proposes a regularized pattern method to represent spatially variant structural features in an image and further exploits a dynamic convolution kernel generation method to match the regularized pattern and improve image reconstruction perform

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Robitzsch, Alexander. "Implementation Aspects in Regularized Structural Equation Models." Algorithms 16, no. 9 (2023): 446. http://dx.doi.org/10.3390/a16090446.

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This article reviews several implementation aspects in estimating regularized single-group and multiple-group structural equation models (SEM). It is demonstrated that approximate estimation approaches that rely on a differentiable approximation of non-differentiable penalty functions perform similarly to the coordinate descent optimization approach of regularized SEMs. Furthermore, using a fixed regularization parameter can sometimes be superior to an optimal regularization parameter selected by the Bayesian information criterion when it comes to the estimation of structural parameters. Moreo

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Robitzsch, 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, no. 1 (2023): 192–208. http://dx.doi.org/10.3390/stats6010012.

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In the social sciences, the performance of two groups is frequently compared based on a cognitive test involving binary items. Item response models are often utilized for comparing the two groups. However, the presence of differential item functioning (DIF) can impact group comparisons. In order to avoid the biased estimation of groups, appropriate statistical methods for handling differential item functioning are required. This article compares the performance-regularized estimation and several robust linking approaches in three simulation studies that address the one-parameter logistic (1PL)

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Zhang, Hong, Dong Lai Hao, and Xiang Yang Liu. "A Precoding Strategy for Massive MIMO System." Applied Mechanics and Materials 568-570 (June 2014): 1278–81. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.1278.

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The computational precoding complexity increases with its dimensions in massive multiple-input multiple-output system. A precoding scheme based on the truncated polynomial expansion is proposed, the hardware implementation is described for the superiority of the algorithm compared with the conventional regularized zero forcing precoding. Finally, under different channel conditions, the simulation results show that the average achievable rate will increase infinitely approaches the regularized zero forcing precoding simulation in a certain order, the polynomial order does not need to scale with

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Leen, Todd K. "From Data Distributions to Regularization in Invariant Learning." Neural Computation 7, no. 5 (1995): 974–81. http://dx.doi.org/10.1162/neco.1995.7.5.974.

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Ideally pattern recognition machines provide constant output when the inputs are transformed under a group G of desired invariances. These invariances can be achieved by enhancing the training data to include examples of inputs transformed by elements of G, while leaving the corresponding targets unchanged. Alternatively the cost function for training can include a regularization term that penalizes changes in the output when the input is transformed under the group. This paper relates the two approaches, showing precisely the sense in which the regularized cost function approximates the resul

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Dissertations / Theses on the topic "Regularized approaches"

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Schwarz, Stephan [Verfasser], Philipp [Gutachter] Junker, and 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.

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Spagnoli, 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/.

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Since the start of 2020 Sars-COVID19 has given rise to a world-wide pandemic. In an attempt to slow down the spreading of this disease various prevention and diagnostic methods have been developed. In this thesis the attention has been put on Machine Learning to predict prognosis based on data originating from radiological images. Radiomics has been used to extract information from images segmented using a software from the hospital which provided both the clinical data and images. The usefulness of different families of variables has then been evaluated through their performance in the metho

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Savino, Mary Edith. "Statistical learning methods for nonlinear geochemical problems." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASM032.

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Dans le cadre de simulations numériques de systèmes géochimiques s'intégrant dans un projet de stockage profond de déchets hautement radioactifs, nous proposons dans cette thèse deux méthodes d'estimation de fonction ainsi qu'une méthode de sélection de variables dans un modèle de régression non-paramétrique multivarié.Plus précisément, dans le Chapitre 2, nous présentons une procédure d'apprentissage actif utilisant les processus Gaussiens pour approcher des fonctions inconnues ayant plusieurs variables d'entrée. Cette méthode permet à chaque itération le calcul de l'incertitude globale sur l

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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.

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<p> X-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 des

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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.

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Due to the rapid development and growing need for information technologies, more and more researchers start to focus on high-dimensional data. Much work has been done on problems like point estimation possessing oracle inequalities, coefficient estimation, variable selection in high-dimensional regression models. However, with respect to the statistical inference for the regression coefficients, there have been few studies. Therefore, we propose a regularized efficient score estimation and testing (RESET) approach for treatment effects in the presence of nuisance parameters, either low-dimensi

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Gürol, 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.

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Cette thèse étudie la méthode du gradient conjugué et la méthode de Lanczos pour la résolution de problèmes aux moindres carrés non-linéaires sous déterminés et régularisés par un terme de pénalisation quadratique. Ces problèmes résultent souvent d'une approche du maximum de vraisemblance, et impliquent un ensemble de m observations physiques et n inconnues estimées par régression non linéaire. Nous supposons ici que n est grand par rapport à m. Un tel cas se présente lorsque des champs tridimensionnels sont estimés à partir d'observations physiques, par exemple dans l'assimilation de données

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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.

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TESEI, 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.

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A rigorous structural analysis is fundamental in the safety assessment of the built heritage and in its efficient conservation and rehabilitation. In line with the necessity of refined techniques, the objective of the present thesis is to develop and validate, in a displacement-based finite element framework, a nonlinear model apt for the study of masonry and concrete structures under monotonic and cyclic loading. The proposed constitutive law adopts two independent scalar damage variables, d+ and d−, in combination with the spectral decomposition of the elastic strain tensor, to simulate th

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Olaya, 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.

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Measles is resurging around the world, and large outbreaks have been observed in several parts of the world. In 2019 the Philippines suffered a major measles outbreak partly due to low immunization rates in certain parts of the population. There is currently limited research on how to identify and reach pockets of unvaccinated individuals effectively. This thesis aims to find important factors associated with non-vaccination against measles using a machine learning approach, using data from the 2017 Philippine National Demographic and Health Survey. In the analyzed sample (n = 4006), 74.84% of

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Salgado, Patarroyo Ivan Camilo. "Spatially Regularized Spherical Reconstruction: A Cross-Domain Filtering Approach for HARDI Signals." Thesis, 2013. http://hdl.handle.net/10012/7847.

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Despite the immense advances of science and medicine in recent years, several aspects regarding the physiology and the anatomy of the human brain are yet to be discovered and understood. A particularly challenging area in the study of human brain anatomy is that of brain connectivity, which describes the intricate means by which different regions of the brain interact with each other. The study of brain connectivity is deeply dependent on understanding the organization of white matter. The latter is predominantly comprised of bundles of myelinated axons, which serve as connecting pathways betw

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Book chapters on the topic "Regularized approaches"

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Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. "Regularization in Reproducing Kernel Hilbert Spaces." In Regularized System Identification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_6.

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AbstractMethods for obtaining a function g in a relationship $$y=g(x)$$ y = g ( x ) from observed samples of y and x are the building blocks for black-box estimation. The classical parametric approach discussed in the previous chapters uses a function model that depends on a finite-dimensional vector, like, e.g., a polynomial model. We have seen that an important issue is the model order choice. This chapter describes some regularization approaches which permit to reconcile flexibility of the model class with well-posedness of the solution exploiting an alternative paradigm to traditional para

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Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. "Numerical Experiments and Real World Cases." In Regularized System Identification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_9.

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AbstractThis chapter collects some numerical experiments to test the performance of kernel-based approaches for discrete-time linear system identification. Using Monte Carlo simulations, we will compare the performance of kernel-based methods with the classical PEM approaches described in Chap. 10.1007/978-3-030-95860-2_2. Simulated and real data are included, concerning a robotic arm, a hairdryer and a problem of temperature prediction. We conclude the chapter by introducing the so-called multi-task learning where several functions (tasks) are simultaneously estimated. This problem is signifi

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Graham, 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. John Benjamins Publishing Company, 2023. http://dx.doi.org/10.1075/ihll.38.04gra.

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Spanish verbs derived via prefixation prescriptively retain the morphological complexity of their root verbs. However, some verbs derived from decir and hacer show allomorphic variation in the future and conditional, which is documented by the RAE for decir but not at all for hacer. The results of this study of historical variation suggest decreased morphological transparency of some verbs but not of others. Verbs derived from hacer continue to resist regularization, with the notable exception of satisfacer. The set of decir-derived verbs is much more complex in its tendencies. This may be att

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Ito, Kazufumi, and Bangti Jin. "Regularized Linear Inversion with Randomized Singular Value Decomposition." In Mathematical and Numerical Approaches for Multi-Wave Inverse Problems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48634-1_5.

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Lombardi, Michele, Federico Baldo, Andrea Borghesi, and Michela Milano. "An Analysis of Regularized Approaches for Constrained Machine Learning." In Trustworthy AI - Integrating Learning, Optimization and Reasoning. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73959-1_11.

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de Campos Souza, Paulo Vitor, Augusto Junio Guimaraes, Vanessa Souza Araujo, Thiago Silva Rezende, and 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. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8614-5_7.

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Schulz, Volker H., and Kathrin Welker. "Shape Optimization for Variational Inequalities of Obstacle Type: Regularized and Unregularized Computational Approaches." In International Series of Numerical Mathematics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79393-7_16.

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Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. "Bias." In Regularized System Identification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_1.

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AbstractAdopting a quadratic loss, the performance of an estimator can be measured in terms of its mean squared error which decomposes into a variance and a bias component. This introductory chapter contains two linear regression examples which describe the importance of designing estimators able to well balance these two components. The first example will deal with estimation of the means of independent Gaussians. We will review the classical least squares approach which, at first sight, could appear the most appropriate solution to the problem. Remarkably, we will instead see that this unbia

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Luo, Ruiyan, Alejandra D. Herrera-Reyes, Yena Kim, Susan Rogowski, Diana White, and Alexandra Smirnova. "Estimation of Time-Dependent Transmission Rate for COVID-19 SVIRD Model Using Predictor–Corrector Algorithm." In Mathematical Modeling for Women’s Health. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58516-6_7.

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AbstractStable parameter estimation is an ongoing challenge within biomathematics, especially in epidemiology. Oftentimes epidemiological models are composed of large numbers of equations and parameters. Due to high dimensionality, classic parameter estimation approaches, such as least square fitting, are computationally expensive. Additionally, the presence of observational noise and reporting errors that accompany real-time data can make these parameter estimation problems ill-posed and unstable. The recent COVID-19 pandemic highlighted the need for efficient parameter estimation tools. In t

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Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. "Bayesian Interpretation of Regularization." In Regularized System Identification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_4.

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AbstractIn the previous chapter, it has been shown that the regularization approach is particularly useful when information contained in the data is not sufficient to obtain a precise estimate of the unknown parameter vector and standard methods, such as least squares, yield poor solutions. The fact itself that an estimate is regarded as poor suggests the existence of some form of prior knowledge on the degree of acceptability of candidate solutions. It is this knowledge that guides the choice of the regularization penalty that is added as a corrective term to the usual sum of squared residual

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Conference papers on the topic "Regularized approaches"

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Hasegawa, Soichiro, Chul-Woo Kim, and Naoki Kawada. "Drive-by road profile identification on highway using vehicle accelerations." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.0364.

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<p>Road profile is one of the most important inspection items for roads and bridges, and an inexpensive and accurate measurement method is desired. In a past study, the authors proposed a method to identify road profile from vehicle acceleration, which solves a regularized least squares minimization by means of a dynamic programming. A regularization parameter, which is a hyperparameter in the least squares problem, is determined by L-curve method. In the previous study, high identification accuracy was confirmed in a limited condition that the driving speed is low as approximately 10km/

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Safari, Habibollah, and Mona Bavarian. "Enhancing Polymer Reaction Engineering Through the Power of Machine Learning." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.157792.

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Copolymers are commonplace in various industries. Nevertheless, fine-tuning their properties bears significant cost and effort. Hence, an ability to predict polymer properties a priori can significantly reduce costs and shorten the need for extensive experimentation. Given that the physical and chemical characteristics of copolymers are correlated with molecular arrangement and chain topology, understanding the reactivity ratios of monomers�which determine the copolymer composition and sequence distribution of monomers in a chain�is important in accelerating research and cutting R&D costs.

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Brault, Dylan, Thomas Olivier, Ferréol Soulez, and Corinne Fournier. "Automation of Gram stain imaging with multispectral in-line holography." In Digital Holography and Three-Dimensional Imaging. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/dh.2024.m3b.2.

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We propose an approach to automate stained micro-biological samples imaging using multispectral in-line holography. The approach is based on a self-calibrated regularized inverse problems reconstruction.

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Kumar, Pankaj, Vishav Pratap Singh, and Ajay Pal Singh. "“A Novel Regularized Deep Learning Approach for Tuberculosis Risk Prediction”." In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). IEEE, 2025. https://doi.org/10.1109/cictn64563.2025.10932404.

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Rahi, Parvez, Sandeep Singh Kang, Ajay Pal Singh, and Inderjeet Singh. "“Liver Disease Risk Prediction Using Regularized Deep Learning: A Novel Approach”." In 2024 2nd International Conference on Advancements and Key Challenges in Green Energy and Computing (AKGEC). IEEE, 2024. https://doi.org/10.1109/akgec62572.2024.10868777.

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Agrawal, R. K., Mohammad Shabbir Alam, Rajesh Kumar A, N. Aparna, Gayathri Devi S, and Amit Jain. "Machine Learning Approach for MRI Brain Tumor Detection using Regularized Extreme Learning." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11070823.

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Houessou, Narech, Sebastian Miron, Karine Gerardin, Bruno Galland, and Philippe Duquenne. "A Tensor Regularized Approach to Spatio-Temporal Pollutant Cartography Applied to Working Environments." In 2024 9th International Conference on Frontiers of Signal Processing (ICFSP). IEEE, 2024. https://doi.org/10.1109/icfsp62546.2024.10785370.

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Raihan Iftee, Md Akil, Raiyan Ashraf, Atiqul Islam, Shahana Akter, and Sunanda Das. "Next-Gen Heart Disease Prediction: A Gradient Boosting Approach with L1-Regularized Neural Networks." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11022378.

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Budillon, Alessandra, Loic Denis, Clement Rambour, Gilda Schirinzi, and 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.

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Xiao, Yichi, Zhe Li, Tianbao Yang, and Lijun Zhang. "SVD-free Convex-Concave Approaches for Nuclear Norm Regularization." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/436.

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Minimizing a convex function of matrices regularized by the nuclear norm arises in many applications such as collaborative filtering and multi-task learning. In this paper, we study the general setting where the convex function could be non-smooth. When the size of the data matrix, denoted by m x n, is very large, existing optimization methods are inefficient because in each iteration, they need to perform a singular value decomposition (SVD) which takes O(m^2 n) time. To reduce the computation cost, we exploit the dual characterization of the nuclear norm to introduce a convex-concave optimiz

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Reports on the topic "Regularized approaches"

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Da Gama Torres, Haroldo. Environmental Implications of Peri-urban Sprawl and the Urbanization of Secondary Cities in Latin America. Inter-American Development Bank, 2011. http://dx.doi.org/10.18235/0008841.

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This paper examines the environmental and social implications of peri-urban growth in small to medium sized cities in Latin America and the Caribbean and proposes approaches to address this challenge. Key recommendations include cities should stimulate strategies for compact growth and efforts to regularize existing irregular settlements should be strongly supported, among other recommendations.

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S.R. Hudson. A Regularized Approach for Solving Magnetic Differential Equations and a Revised Iterative Equilibrium Algorithm. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/990749.

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