Relevant bibliographies by topics / Support Vector Machine Regression

Contents

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

Academic literature on the topic 'Support Vector Machine Regression'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Support Vector Machine Regression"

1

GUO, Hu-Sheng, and Wen-Jian WANG. "Dynamical Granular Support Vector Regression Machine." Journal of Software 24, no. 11 (2014): 2535–47. http://dx.doi.org/10.3724/sp.j.1001.2013.04472.

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Sun, Shaochao, and Dao Huang. "Flatheaded Support Vector Machine for Regression." Advanced Science Letters 19, no. 8 (2013): 2293–99. http://dx.doi.org/10.1166/asl.2013.4907.

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Wang, Jian Guo, Liang Wu Cheng, Wen Xing Zhang, and Bo Qin. "A Modified Incremental Support Vector Machine for Regression." Applied Mechanics and Materials 135-136 (October 2011): 63–69. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.63.

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support vector machine (SVM) has been shown to exhibit superior predictive power compared to traditional approaches in many studies, such as mechanical equipment monitoring and diagnosis. However, SVM training is very costly in terms of time and memory consumption due to the enormous amounts of training data and the quadratic programming problem. In order to improve SVM training speed and accuracy, we propose a modified incremental support vector machine (MISVM) for regression problems in this paper. The main concepts are that using the distance from the margin vectors which violate the Karush
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ZHENG, SHENG, YUQIU SUN, JINWEN TIAN, and JAIN LIU. "MAPPED LEAST SQUARES SUPPORT VECTOR MACHINE REGRESSION." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 03 (2005): 459–75. http://dx.doi.org/10.1142/s0218001405004058.

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This paper describes a novel version of regression SVM (Support Vector Machines) that is based on the least-squares error. We show that the solution of this optimization problem can be obtained easily once the inverse of a certain matrix is computed. This matrix, however, depends only on the input vectors, but not on the labels. Thus, if many learning problems with the same set of input vectors but different sets of labels have to be solved, it makes sense to compute the inverse of the matrix just once and then use it for computing all subsequent models. The computational complexity to train a
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Lin-Kai Luo, Lin-Kai Luo, Chao-Jie Xu Lin-Kai Luo, Ling-Jun Ye Chao-Jie Xu, and Hong Peng Ling-Jun Ye. "Some Support Vector Regression Machines with Given Empirical Risks Partly." 電腦學刊 33, no. 5 (2022): 061–72. http://dx.doi.org/10.53106/199115992022103305006.

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<p>There are often some prior requirements about empirical risk in regression problems. To meet these requirements, this paper firstly proposes two novel support vector regression machine models in which part of empirical risks are given. One is a support vector regression machine in which partial empirical risks are given (PSVR), and the other is a model in which unilateral partial empirical risks are given (UPSVR). For the samples with given empirical risk levels, PSVR meets the requirements by some inequality constraints about empirical risk levels, while for the other samples without
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Besalatpour, A., M. Hajabbasi, S. Ayoubi, A. Gharipour, and A. Jazi. "Prediction of soil physical properties by optimized support vector machines." International Agrophysics 26, no. 2 (2012): 109–15. http://dx.doi.org/10.2478/v10247-012-0017-7.

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Prediction of soil physical properties by optimized support vector machinesThe potential use of optimized support vector machines with simulated annealing algorithm in developing prediction functions for estimating soil aggregate stability and soil shear strength was evaluated. The predictive capabilities of support vector machines in comparison with traditional regression prediction functions were also studied. In results, the support vector machines achieved greater accuracy in predicting both soil shear strength and soil aggregate stability properties comparing to traditional multiple-linea
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Arjmandzadeh, Ameneh, Sohrab Effati, and Mohammad Zamirian. "Interval Support Vector Machine In Regression Analysis." Journal of Mathematics and Computer Science 02, no. 03 (2011): 565–71. http://dx.doi.org/10.22436/jmcs.02.03.19.

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熊, 令纯. "Five Understandings on Support Vector Machine Regression." Hans Journal of Data Mining 09, no. 02 (2019): 52–59. http://dx.doi.org/10.12677/hjdm.2019.92007.

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Rastogi (nee Khemchandani), Reshma, Pritam Anand, and Suresh Chandra. "-norm Twin Support Vector Machine-based Regression." Optimization 66, no. 11 (2017): 1895–911. http://dx.doi.org/10.1080/02331934.2017.1364739.

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Khemchandani, Reshma, Keshav Goyal, and Suresh Chandra. "TWSVR: Regression via Twin Support Vector Machine." Neural Networks 74 (February 2016): 14–21. http://dx.doi.org/10.1016/j.neunet.2015.10.007.

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Dissertations / Theses on the topic "Support Vector Machine Regression"

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Lee, Keun Joo. "Geometric Tolerancing of Cylindricity Utilizing Support Vector Regression." Scholarly Repository, 2009. http://scholarlyrepository.miami.edu/oa_theses/233.

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In the age where quick turn around time and high speed manufacturing methods are becoming more important, quality assurance is a consistent bottleneck in production. With the development of cheap and fast computer hardware, it has become viable to use machine vision for the collection of data points from a machined part. The generation of these large sample points have necessitated a need for a comprehensive algorithm that will be able to provide accurate results while being computationally efficient. Current established methods are least-squares (LSQ) and non-linear programming (NLP). The LSQ
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Wu, Zhili. "Regularization methods for support vector machines." HKBU Institutional Repository, 2008. http://repository.hkbu.edu.hk/etd_ra/912.

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Shah, Rohan Shiloh. "Support vector machines for classification and regression." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=100247.

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In the last decade Support Vector Machines (SVMs) have emerged as an important learning technique for solving classification and regression problems in various fields, most notably in computational biology, finance and text categorization. This is due in part to built-in mechanisms to ensure good generalization which leads to accurate prediction, the use of kernel functions to model non-linear distributions, the ability to train relatively quickly on large data sets using novel mathematical optimization techniques and most significantly the possibility of theoretical analysis using computation
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OLIVEIRA, A. B. "Modelo de Predição para análise comparativa de Técnicas Neuro-Fuzzy e de Regressão." Universidade Federal do Espírito Santo, 2010. http://repositorio.ufes.br/handle/10/4218.

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Made available in DSpace on 2016-08-29T15:33:12Z (GMT). No. of bitstreams: 1 tese_3521_.pdf: 2782962 bytes, checksum: d4b2294e5ee9ab86b7a35aec083af692 (MD5) Previous issue date: 2010-02-12<br>Os Modelos de Predição implementados pelos algoritmos de Aprendizagem de Máquina advindos como linha de pesquisa da Inteligência Computacional são resultantes de pesquisas e investigações empíricas em dados do mundo real. Neste contexto; estes modelos são extraídos para comparação de duas grandes técnicas de aprendizagem de máquina Redes Neuro-Fuzzy e de Regressão aplicadas no intuito de estimar um pa
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Wågberg, Max. "Att förutspå Sveriges bistånd : En jämförelse mellan Support Vector Regression och ARIMA." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36479.

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In recent years, the use of machine learning has increased significantly. Its uses range from making the everyday life easier with voice-guided smart devices to image recognition, or predicting the stock market. Predicting economic values has long been possible by using methods other than machine learning, such as statistical algorithms. These algorithms and machine learning models use time series, which is a set of data points observed constantly over a given time interval, in order to predict data points beyond the original time series. But which of these methods gives the best results? The
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Uslan, Volkan. "Support vector machine-based fuzzy systems for quantitative prediction of peptide binding affinity." Thesis, De Montfort University, 2015. http://hdl.handle.net/2086/11170.

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Reliable prediction of binding affinity of peptides is one of the most challenging but important complex modelling problems in the post-genome era due to the diversity and functionality of the peptides discovered. Generally, peptide binding prediction models are commonly used to find out whether a binding exists between a certain peptide(s) and a major histocompatibility complex (MHC) molecule(s). Recent research efforts have been focused on quantifying the binding predictions. The objective of this thesis is to develop reliable real-value predictive models through the use of fuzzy systems. A
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Hasanov, Ilgar <1996&gt. "A Comparison between Support Vector Machines and Logistic Regression for Classification." Master's Degree Thesis, Università Ca' Foscari Venezia, 2022. http://hdl.handle.net/10579/20753.

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This dissertation is about classification methods for binary data developed in Computer Science and Statistics. The research focuses on two main algorithms called support vector machines and logistic regression. The thesis consists of three chapters. The first chapter provides a general discussion of classification algorithms used in Statistical and Machine Learning with special emphasis on logistic regression and support vector machines. The second chapter includes some simulation studies to compare the classification methods. The third chapter concludes the thesis with an application to a re
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Lee, Ho-Jin. "Functional data analysis: classification and regression." Texas A&M University, 2004. http://hdl.handle.net/1969.1/2805.

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Functional data refer to data which consist of observed functions or curves evaluated at a finite subset of some interval. In this dissertation, we discuss statistical analysis, especially classification and regression when data are available in function forms. Due to the nature of functional data, one considers function spaces in presenting such type of data, and each functional observation is viewed as a realization generated by a random mechanism in the spaces. The classification procedure in this dissertation is based on dimension reduction techniques of the spaces. One commonly used metho
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Hechter, Trudie. "A comparison of support vector machines and traditional techniques for statistical regression and classification." Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/49810.

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Thesis (MComm)--Stellenbosch University, 2004.<br>ENGLISH ABSTRACT: Since its introduction in Boser et al. (1992), the support vector machine has become a popular tool in a variety of machine learning applications. More recently, the support vector machine has also been receiving increasing attention in the statistical community as a tool for classification and regression. In this thesis support vector machines are compared to more traditional techniques for statistical classification and regression. The techniques are applied to data from a life assurance environment for a binary class
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Thorén, Daniel. "Radar based tank level measurement using machine learning : Agricultural machines." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176259.

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Agriculture is becoming more dependent on computerized solutions to make thefarmer’s job easier. The big step that many companies are working towards is fullyautonomous vehicles that work the fields. To that end, the equipment fitted to saidvehicles must also adapt and become autonomous. Making this equipment autonomoustakes many incremental steps, one of which is developing an accurate and reliable tanklevel measurement system. In this thesis, a system for tank level measurement in a seedplanting machine is evaluated. Traditional systems use load cells to measure the weightof the tank however
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Books on the topic "Support Vector Machine Regression"

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Drezet, P. Directly optimized support vector machines for classification and regression. University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1998.

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Campbell, Colin. Learning with support vector machines. Morgan & Claypool, 2011.

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Hamel, Lutz. Knowledge discovery with support vector machines. John Wiley & Sons, 2009.

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Boyle, Brandon H. Support vector machines: Data analysis, machine learning, and applications. Nova Science Publishers, 2011.

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K, Suykens Johan A., Signoretto Marco, and Argyriou Andreas, eds. Regularization, optimization, kernels, and support vector machines. Taylor & Francis, 2014.

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Bernhard, Schölkopf, Burges Christopher J. C, and Smola Alexander J, eds. Advances in kernel methods: Support vector learning. MIT Press, 1999.

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Joachims, Thorsten. Learning to classify text using support vector machines. Kluwer Academic Publishers, 2002.

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Ertekin, Şeyda. Algorithms for efficient learning systems: Online and active learning approaches. VDM Verlag Dr. Müller, 2009.

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J, Smola Alexander, ed. Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT Press, 2002.

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Terzic, Jenny. Ultrasonic Fluid Quantity Measurement in Dynamic Vehicular Applications: A Support Vector Machine Approach. Springer International Publishing, 2013.

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Book chapters on the topic "Support Vector Machine Regression"

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Support Vector Machines and Support Vector Regression." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_9.

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AbstractIn this chapter, the support vector machines (svm) methods are studied. We first point out the origin and popularity of these methods and then we define the hyperplane concept which is the key for building these methods. We derive methods related to svm: the maximum margin classifier and the support vector classifier. We describe the derivation of the svm along with some kernel functions that are fundamental for building the different kernels methods that are allowed in svm. We explain how the svm for binary response variables can be expanded for categorical response variables and give
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Awad, Mariette, and Rahul Khanna. "Support Vector Regression." In Efficient Learning Machines. Apress, 2015. http://dx.doi.org/10.1007/978-1-4302-5990-9_4.

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Schleif, Frank-Michael. "Indefinite Support Vector Regression." In Artificial Neural Networks and Machine Learning – ICANN 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68612-7_36.

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Jayadeva, Reshma Khemchandani, and Suresh Chandra. "TWSVR: Twin Support Vector Machine Based Regression." In Twin Support Vector Machines. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46186-1_4.

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Berk, Richard A. "Support Vector Machines." In Statistical Learning from a Regression Perspective. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44048-4_7.

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Berk, Richard A. "Support Vector Machines." In Statistical Learning from a Regression Perspective. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40189-4_7.

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Ullrich, Katrin, Michael Kamp, Thomas Gärtner, Martin Vogt, and Stefan Wrobel. "Co-Regularised Support Vector Regression." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71246-8_21.

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Martin, Mario. "On-Line Support Vector Machine Regression." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36755-1_24.

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Christmann, Andreas. "Regression depth and support vector machine." In DIMACS Series in Discrete Mathematics and Theoretical Computer Science. American Mathematical Society, 2006. http://dx.doi.org/10.1090/dimacs/072/06.

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Díaz-Vico, David, Jesús Prada, Adil Omari, and José R. Dorronsoro. "Deep Support Vector Classification and Regression." In From Bioinspired Systems and Biomedical Applications to Machine Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19651-6_4.

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Conference papers on the topic "Support Vector Machine Regression"

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Srivastava, Ansh, Mrigaannkaa Singh, and Somesh Nandi. "Support Vector Regression Based Traffic Prediction Machine Learning Model*." In 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2024. https://doi.org/10.1109/csitss64042.2024.10816969.

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Misra, Praveen Kumar, Rahul Gupta, and Barkha Gupta. "Diabetes Prediction using Logistic Regression and Support Vector Machine (SVM) Classifier." In 2024 International Conference on Signal Processing and Advance Research in Computing (SPARC). IEEE, 2024. https://doi.org/10.1109/sparc61891.2024.10829296.

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Khemchandani, Reshma, Keshav Goyal, and Suresh Chandra. "Twin Support Vector Machine based Regression." In 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR). IEEE, 2015. http://dx.doi.org/10.1109/icapr.2015.7050651.

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Shen, Jin-Dong. "New smooth support vector machine for regression." In 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6358931.

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Hao, Pei-Yi. "Possibilistic regression analysis by support vector machine." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007433.

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Xue, Zhenxia, and Wanli Liu. "A fuzzy rough support vector regression machine." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6234232.

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Stoean, Ruxandra, D. Dumitrescu, Mike Preuss, and Catalin Stoean. "Evolutionary Support Vector Regression Machines." In 2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. IEEE, 2006. http://dx.doi.org/10.1109/synasc.2006.39.

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Dilmen, Erdem, and Selami Beyhan. "Deep recurrent support vector machine for online regression." In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2017. http://dx.doi.org/10.1109/idap.2017.8090243.

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Fu, Guanghui, and Guanghua Hu. "Total Least Square Support Vector Machine for Regression." In 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2008. http://dx.doi.org/10.1109/icicta.2008.134.

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Zhang, Hong, and Yongmei Lei. "BSP-based support vector regression machine parallel framework." In 2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS). IEEE, 2013. http://dx.doi.org/10.1109/icis.2013.6607862.

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Reports on the topic "Support Vector Machine Regression"

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Karlsson, Hyunjoo Kim, and Yushu Li. Investigation of Swedish krona exchange rate volatilityby APARCH-Support Vector Regression. Department of Economics and Statistics, Linnaeus University, 2024. http://dx.doi.org/10.15626/ns.wp.2024.10.

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This paper investigates daily exchange rate volatility behaviors with a focus on a small open economy’s currency, the Swedish krona (SEK), against four currencies: the U.S. dollar, Euro, the Pound Sterling (GBP), and the Norwegian krone (NOK) over the whole period from Jan. 2010 to March 2023, whereas the whole period is divided into different sub-sample periods based on the economic events. In the framework of APARCH models, we find that volatility behavior of the Swedish krona (SEK) exchange rates varies across different currency pairs (SEK being included in all cases) and sub-sample periods
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Puttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, 2020. http://dx.doi.org/10.22617/wps200434-2.

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This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. It also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yiel
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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Gertz, E. M., and J. D. Griffin. Support vector machine classifiers for large data sets. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/881587.

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Alali, Ali. Application of Support Vector Machine in Predicting the Market's Monthly Trend Direction. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.1495.

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O'Neill, Francis, Kristofer Lasko, and Elena Sava. Snow-covered region improvements to a support vector machine-based semi-automated land cover mapping decision support tool. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45842.

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This work builds on the original semi-automated land cover mapping algorithm and quantifies improvements to class accuracy, analyzes the results, and conducts a more in-depth accuracy assessment in conjunction with test sites and the National Land Cover Database (NLCD). This algorithm uses support vector machines trained on data collected across the continental United States to generate a pre-trained model for inclusion into a decision support tool within ArcGIS Pro. Version 2 includes an additional snow cover class and accounts for snow cover effects within the other land cover classes. Overa
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Arun, Ramaiah, and Shanmugasundaram Singaravelan. Classification of Brain Tumour in Magnetic Resonance Images Using Hybrid Kernel Based Support Vector Machine. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2019. http://dx.doi.org/10.7546/crabs.2019.10.12.

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Liu, Y. Support vector machine for the prediction of future trend of Athabasca River (Alberta) flow rate. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/299739.

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Qi, Yuan. Learning Algorithms for Audio and Video Processing: Independent Component Analysis and Support Vector Machine Based Approaches. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada458739.

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Luo, Yuzhou, Rui Wang, Zhongwei Jiang, and Xiqing Zuo. Assessment of the Effect of Health Monitoring System on the Sleep Quality by Using Support Vector Machine. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.01.16.

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