Literatura académica sobre el tema "Least-squares support vector machine"
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Artículos de revistas sobre el tema "Least-squares support vector machine"
KITAYAMA, Satoshi, Masao ARAKAWA y Koetsu YAMAZAKI. "1403 Least-Squares Support Vector Machine". Proceedings of Design & Systems Conference 2010.20 (2010): _1403–1_—_1403–5_. http://dx.doi.org/10.1299/jsmedsd.2010.20._1403-1_.
Texto completoAdankon, M. M., M. Cheriet y A. Biem. "Semisupervised Least Squares Support Vector Machine". IEEE Transactions on Neural Networks 20, n.º 12 (diciembre de 2009): 1858–70. http://dx.doi.org/10.1109/tnn.2009.2031143.
Texto completoZHENG, SHENG, YUQIU SUN, JINWEN TIAN y JAIN LIU. "MAPPED LEAST SQUARES SUPPORT VECTOR MACHINE REGRESSION". International Journal of Pattern Recognition and Artificial Intelligence 19, n.º 03 (mayo de 2005): 459–75. http://dx.doi.org/10.1142/s0218001405004058.
Texto completoHwang, Changha y Jooyong Shim. "Geographically weighted least squares-support vector machine". Journal of the Korean Data and Information Science Society 28, n.º 1 (31 de enero de 2017): 227–35. http://dx.doi.org/10.7465/jkdi.2017.28.1.227.
Texto completoChoi, Young-Sik. "Least squares one-class support vector machine". Pattern Recognition Letters 30, n.º 13 (octubre de 2009): 1236–40. http://dx.doi.org/10.1016/j.patrec.2009.05.007.
Texto completoHuang, Xiaolin, Lei Shi y Johan A. K. Suykens. "Asymmetric least squares support vector machine classifiers". Computational Statistics & Data Analysis 70 (febrero de 2014): 395–405. http://dx.doi.org/10.1016/j.csda.2013.09.015.
Texto completoLiu, Dalian, Yong Shi, Yingjie Tian y Xiankai Huang. "Ramp loss least squares support vector machine". Journal of Computational Science 14 (mayo de 2016): 61–68. http://dx.doi.org/10.1016/j.jocs.2016.02.001.
Texto completovan Gestel, Tony, Johan A. K. Suykens, Bart Baesens, Stijn Viaene, Jan Vanthienen, Guido Dedene, Bart de Moor y Joos Vandewalle. "Benchmarking Least Squares Support Vector Machine Classifiers". Machine Learning 54, n.º 1 (enero de 2004): 5–32. http://dx.doi.org/10.1023/b:mach.0000008082.80494.e0.
Texto completoZhang, Yong Li, Yan Wei Zhu, Shu Fei Lin, Xiu Juan Sun, Qiu Na Zhang y Xiao Hong Liu. "Algorithm of Sparse Least Squares Support Vector Machine". Advanced Materials Research 143-144 (octubre de 2010): 1229–33. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.1229.
Texto completoDong, Zengshou, Zhaojing Ren y You Dong. "MECHANICAL FAULT RECOGNITION RESEARCH BASED ON LMD-LSSVM". Transactions of the Canadian Society for Mechanical Engineering 40, n.º 4 (noviembre de 2016): 541–49. http://dx.doi.org/10.1139/tcsme-2016-0042.
Texto completoTesis sobre el tema "Least-squares support vector machine"
Zigic, Ljiljana. "Direct L2 Support Vector Machine". VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4274.
Texto completoLi, Ke. "Automotive engine tuning using least-squares support vector machines and evolutionary optimization". Thesis, University of Macau, 2012. http://umaclib3.umac.mo/record=b2580667.
Texto completoKhawaja, Taimoor Saleem. "A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34758.
Texto completoErdas, Ozlem. "Modelling And Predicting Binding Affinity Of Pcp-like Compounds Using Machine Learning Methods". Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12608792/index.pdf.
Texto completoPai, Chih-Yun. "Automatic Pain Assessment from Infants’ Crying Sounds". Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6560.
Texto completoYoldas, Mine. "Predicting The Effect Of Hydrophobicity Surface On Binding Affinity Of Pcp-like Compounds Using Machine Learning Methods". Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613215/index.pdf.
Texto completoTREVISO, FELIPE. "Modeling for the Computer-Aided Design of Long Interconnects". Doctoral thesis, Politecnico di Torino, 2022. https://hdl.handle.net/11583/2973429.
Texto completoMelo, Davyd Bandeira de. "Algoritmos de aprendizagem para aproximaÃÃo da cinemÃtica inversa de robÃs manipuladores: um estudo comparativo". Universidade Federal do CearÃ, 2015. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=16997.
Texto completoNesta dissertaÃÃo sÃo reportados os resultados de um amplo estudo comparativo envolvendo sete algoritmos de aprendizado de mÃquinas aplicados à tarefa de aproximaÃÃo do modelo cinemÃtico inverso de 3 robÃs manipuladores (planar, PUMA 560 e Motoman HP6). Os algoritmos avaliados sÃo os seguintes: Perceptron Multicamadas (MLP), MÃquina de Aprendizado Extremo (ELM), RegressÃo de MÃnimos Quadrados via Vetores-Suporte (LS-SVR), MÃquina de Aprendizado MÃnimo (MLM), Processos Gaussianos (PG), Sistema de InferÃncia Fuzzy Baseado em Rede Adaptativa (ANFIS) e Mapeamento Linear Local (LLM). Estes algoritmos sÃo avaliados quanto à acurÃcia na estimaÃÃo dos Ãngulos das juntas dos robÃs manipuladores em experimentos envolvendo a geraÃÃo de vÃrios tipos de trajetÃrias no volume de trabalho dos referidos robÃs. Uma avaliaÃÃo abrangente do desempenho de cada algoritmo à feito com base na anÃlise dos resÃduos e testes de hipÃteses sÃo executados para verificar se hà diferenÃas significativas entre os desempenhos dos melhores algoritmos.
Padilha, Carlos Alberto de Araújo. "Uma abordagem multinível usando algoritmos genéticos em um comitê de LS-SVM". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/174541.
Texto completoMany years ago, the ensemble systems have been shown to be an efficient method to increase the accuracy and stability of learning algorithms in recent decades, although its construction has a question to be elucidated: diversity. The disagreement among the models that compose the ensemble can be generated when they are built under different circumstances, such as training dataset, parameter setting and selection of learning algorithms. The ensemble may be viewed as a structure with three levels: input space, the base components and the combining block of the components responses. In this work is proposed a multi-level approach using genetic algorithms to build the ensemble of Least Squares Support Vector Machines (LS-SVM), performing a feature selection in the input space, the parameterization and the choice of which models will compose the ensemble at the component level and finding a weight vector which best represents the importance of each classifier in the final response of the ensemble. In order to evaluate the performance of the proposed approach, some benchmarks from UCI Repository have been used to compare with other classification algorithms. Also, the results obtained by our approach were compared with some deep learning methods on the datasets MNIST and CIFAR and proved very satisfactory.
SEDAGHAT, MOSTAFA. "Modeling and Optimization of the Microwave PCB Interconnects Using Macromodel Techniques". Doctoral thesis, Politecnico di Torino, 2022. https://hdl.handle.net/11583/2973989.
Texto completoLibros sobre el tema "Least-squares support vector machine"
missing], [name. Least squares support vector machines. Singapore: World Scientific, 2002.
Buscar texto completoLeast squares support vector machines. River Edge, NJ: World Scientific, 2002.
Buscar texto completoVandewalle, Joos, Bart De Moor, Tony Van Gestel, Jos De Brabanter y Johan A. K. Suykens. Least Squares Support Vector Machines. World Scientific Publishing Company, 2003.
Buscar texto completoO. Görgülü y A. Akilli. Egg production curve fitting using least square support vector machines and nonlinear regression analysis. Verlag Eugen Ulmer, 2018. http://dx.doi.org/10.1399/eps.2018.235.
Texto completoCapítulos de libros sobre el tema "Least-squares support vector machine"
Pelckmans, K., I. Goethals, J. D. Brabanter, J. A. K. Suykens y B. D. Moor. "Componentwise Least Squares Support Vector Machines". En Support Vector Machines: Theory and Applications, 77–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/10984697_3.
Texto completoZhang, Xiaoou y Zexuan Zhu. "Sparse Multi-task Least-Squares Support Vector Machine". En Neural Computing for Advanced Applications, 157–67. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7670-6_14.
Texto completoLi, Yang y Wanmei Tang. "A least Squares Support Vector Machine Sparseness Algorithm". En Lecture Notes in Electrical Engineering, 346–53. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2386-6_45.
Texto completoWu, Fangfang y Yinliang Zhao. "Least Squares Littlewood-Paley Wavelet Support Vector Machine". En Lecture Notes in Computer Science, 462–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11579427_47.
Texto completoLi, Lijuan, Youfeng Li, Hongye Su y Jian Chu. "Least Squares Support Vector Machines Based on Support Vector Degrees". En Lecture Notes in Computer Science, 1275–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11816157_160.
Texto completoGan, Liang-zhi, Hai-kuan Liu y You-xian Sun. "Sparse Least Squares Support Vector Machine for Function Estimation". En Advances in Neural Networks - ISNN 2006, 1016–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759966_149.
Texto completoLópez, Jorge, Álvaro Barbero y José R. Dorronsoro. "Momentum Acceleration of Least–Squares Support Vector Machines". En Lecture Notes in Computer Science, 135–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21738-8_18.
Texto completoGijsberts, Arjan, Giorgio Metta y Léon Rothkrantz. "Evolutionary Optimization of Least-Squares Support Vector Machines". En Annals of Information Systems, 277–97. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-1280-0_12.
Texto completoLi, You-Feng, Li-Juan Li, Hong-Ye Su y Jian Chu. "Least Squares Support Vector Machine Based Partially Linear Model Identification". En Lecture Notes in Computer Science, 775–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11816157_94.
Texto completoZhang, Yongli, Yanwei Zhu, Shufei Lin y Xiaohong Liu. "Application of Least Squares Support Vector Machine in Fault Diagnosis". En Communications in Computer and Information Science, 192–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27452-7_26.
Texto completoActas de conferencias sobre el tema "Least-squares support vector machine"
"Least squares support vector machine ensemble". En 2004 IEEE International Joint Conference on Neural Networks. IEEE, 2004. http://dx.doi.org/10.1109/ijcnn.2004.1380924.
Texto completoYugang Fan, Ping Li y Zhihuan Song. "Dynamic Least Squares Support Vector Machine". En 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1713313.
Texto completoKong, Rui y Bing Zhang. "A Fast Least Squares Support Vector Machine classifier". En 2008 Chinese Control and Decision Conference (CCDC). IEEE, 2008. http://dx.doi.org/10.1109/ccdc.2008.4597413.
Texto completoJafar, Nurkamila, Sri Astuti Thamrin y Armin Lawi. "Multiclass classification using Least Squares Support Vector Machine". En 2016 International Conference on Computational Intelligence and Cybernetics (CYBERNETICSCOM). IEEE, 2016. http://dx.doi.org/10.1109/cyberneticscom.2016.7892558.
Texto completoYanhui, Zhang, Liu Binbin y Pan Zhongming. "Biofouling estimation with least squares support vector machine". En 2016 IEEE International Conference on Information and Automation (ICIA). IEEE, 2016. http://dx.doi.org/10.1109/icinfa.2016.7831980.
Texto completoYongsheng Sang, Haixian Zhang y Lin Zuo. "Least Squares Support Vector Machine classifiers using PCNNs". En 2008 IEEE Conference on Cybernetics and Intelligent Systems (CIS). IEEE, 2008. http://dx.doi.org/10.1109/iccis.2008.4670890.
Texto completoXia, Xiao-Lei. "A novel sparse least-squares support vector machine". En 2012 5th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2012. http://dx.doi.org/10.1109/bmei.2012.6513100.
Texto completoGaobo, Chen y Chen Xiufang. "Combining partial least squares regression and least squares support vector machine for data mining". En 2011 International Conference on E-Business and E-Government (ICEE). IEEE, 2011. http://dx.doi.org/10.1109/icebeg.2011.5881755.
Texto completoJing, Lv y Zhang Yanqing. "Colleges Employment Forecasting by Least Squares Support Vector Machine". En 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE). IEEE, 2012. http://dx.doi.org/10.1109/iccsee.2012.455.
Texto completoRichhariya, B. y M. Tanveer. "Universum least squares twin parametric-margin support vector machine". En 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206865.
Texto completoInformes sobre el tema "Least-squares support vector machine"
Puttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante y Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, diciembre de 2020. http://dx.doi.org/10.22617/wps200434-2.
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