Academic literature on the topic 'Support Vector Machine'
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Journal articles on the topic "Support Vector Machine"
Xia, Tian. "Support Vector Machine Based Educational Resources Classification." International Journal of Information and Education Technology 6, no. 11 (2016): 880–83. http://dx.doi.org/10.7763/ijiet.2016.v6.809.
Full textBE, R. Aruna Sankari. "Cervical Cancer Detection Using Support Vector Machine." International journal of Emerging Trends in Science and Technology 04, no. 03 (March 31, 2017): 5033–38. http://dx.doi.org/10.18535/ijetst/v4i3.08.
Full textHeo, Gyeong-Yong, and Seong-Hoon Kim. "Context-Aware Fusion with Support Vector Machine." Journal of the Korea Society of Computer and Information 19, no. 6 (June 30, 2014): 19–26. http://dx.doi.org/10.9708/jksci.2014.19.6.019.
Full textHuimin, Yao. "Research on Parallel Support Vector Machine Based on Spark Big Data Platform." Scientific Programming 2021 (December 17, 2021): 1–9. http://dx.doi.org/10.1155/2021/7998417.
Full textV., Dr Padmanabha Reddy. "Human Cognitive State classification using Support Vector Machine." Journal of Advanced Research in Dynamical and Control Systems 12, no. 01-Special Issue (February 13, 2020): 46–54. http://dx.doi.org/10.5373/jardcs/v12sp1/20201045.
Full textJung, Kang-Mo. "Robust Algorithm for Multiclass Weighted Support Vector Machine." SIJ Transactions on Advances in Space Research & Earth Exploration 4, no. 3 (June 10, 2016): 1–5. http://dx.doi.org/10.9756/sijasree/v4i3/0203430402.
Full textDhaifallah, Mujahed Al, and K. S. Nisar. "Support Vector Machine Identification of Subspace Hammerstein Models." International Journal of Computer Theory and Engineering 7, no. 1 (February 2014): 9–15. http://dx.doi.org/10.7763/ijcte.2015.v7.922.
Full textYANG, Zhi-Min, Yuan-Hai SHAO, and Jing LIANG. "Unascertained Support Vector Machine." Acta Automatica Sinica 39, no. 6 (March 25, 2014): 895–901. http://dx.doi.org/10.3724/sp.j.1004.2013.00895.
Full textZhang, L., W. Zhou, and L. Jiao. "Wavelet Support Vector Machine." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 1 (February 2004): 34–39. http://dx.doi.org/10.1109/tsmcb.2003.811113.
Full textNavia-Vázquez, A., and E. Parrado-Hernández. "Support vector machine interpretation." Neurocomputing 69, no. 13-15 (August 2006): 1754–59. http://dx.doi.org/10.1016/j.neucom.2005.12.118.
Full textDissertations / Theses on the topic "Support Vector Machine"
Cardamone, Dario. "Support Vector Machine a Machine Learning Algorithm." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.
Find full textMcChesney, Charlie. "External Support Vector Machine Clustering." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/409.
Full textArmond, Kenneth C. Jr. "Distributed Support Vector Machine Learning." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/711.
Full textZigic, Ljiljana. "Direct L2 Support Vector Machine." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4274.
Full textPark, Yongwon Baskiyar Sanjeev. "Dynamic task scheduling onto heterogeneous machines using Support Vector Machine." Auburn, Ala, 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SPRING/Computer_Science_and_Software_Engineering/Thesis/Park_Yong_50.pdf.
Full textTsang, Wai-Hung. "Scaling up support vector machines /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20TSANG.
Full textPerez, Daniel Antonio. "Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34858.
Full textWen, Tong 1970. "Support Vector Machine algorithms : analysis and applications." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8404.
Full textIncludes bibliographical references (p. 89-97).
Support Vector Machines (SVMs) have attracted recent attention as a learning technique to attack classification problems. The goal of my thesis work is to improve computational algorithms as well as the mathematical understanding of SVMs, so that they can be easily applied to real problems. SVMs solve classification problems by learning from training examples. From the geometry, it is easy to formulate the finding of SVM classifiers as a linearly constrained Quadratic Programming (QP) problem. However, in practice its dual problem is actually computed. An important property of the dual QP problem is that its solution is sparse. The training examples that determine the SVM classifier are known as support vectors (SVs). Motivated by the geometric derivation of the primal QP problem, we investigate how the dual problem is related to the geometry of SVs. This investigation leads to a geometric interpretation of the scaling property of SVMs and an algorithm to further compress the SVs. A random model for the training examples connects the Hessian matrix of the dual QP problem to Wishart matrices. After deriving the distributions of the elements of the inverse Wishart matrix Wn-1(n, nI), we give a conjecture about the summation of the elements of Wn-1(n, nI). It becomes challenging to solve the dual QP problem when the training set is large. We develop a fast algorithm for solving this problem. Numerical experiments show that the MATLAB implementation of this projected Conjugate Gradient algorithm is competitive with benchmark C/C++ codes such as SVMlight and SvmFu. Furthermore, we apply SVMs to time series data.
(cont.) In this application, SVMs are used to predict the movement of the stock market. Our results show that using SVMs has the potential to outperform the solution based on the most widely used geometric Brownian motion model of stock prices.
by Tong Wen.
Ph.D.
Liu, Yufeng. "Multicategory psi-learning and support vector machine." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1085424065.
Full textTitle from first page of PDF file. Document formatted into pages; contains x, 71 p.; also includes graphics Includes bibliographical references (p. 69-71). Available online via OhioLINK's ETD Center
Merat, Sepehr. "Clustering Via Supervised Support Vector Machines." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/857.
Full textBooks on the topic "Support Vector Machine"
Andreas, Christmann, ed. Support vector machines. New York: Springer, 2008.
Find full textCampbell, Colin. Learning with support vector machines. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.
Find full textJoachim, Diederich, ed. Rule extraction from support vector machines. Berlin: Springer, 2008.
Find full textBoyle, Brandon H. Support vector machines: Data analysis, machine learning, and applications. Hauppauge, N.Y: Nova Science Publishers, 2011.
Find full textHamel, Lutz. Knowledge discovery with support vector machines. Hoboken, N.J: John Wiley & Sons, 2009.
Find full textmissing], [name. Least squares support vector machines. Singapore: World Scientific, 2002.
Find full textErtekin, Şeyda. Algorithms for efficient learning systems: Online and active learning approaches. Saarbrücken: VDM Verlag Dr. Müller, 2009.
Find full textSupport vector machines for pattern classification. 2nd ed. London: Springer, 2010.
Find full textJoachims, Thorsten. Learning to classify text using support vector machines. Boston: Kluwer Academic Publishers, 2002.
Find full textK, Suykens Johan A., Signoretto Marco, and Argyriou Andreas, eds. Regularization, optimization, kernels, and support vector machines. Boca Raton: Taylor & Francis, 2014.
Find full textBook chapters on the topic "Support Vector Machine"
Zhou, Zhi-Hua. "Support Vector Machine." In Machine Learning, 129–53. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1967-3_6.
Full textZhang, Dengsheng. "Support Vector Machine." In Texts in Computer Science, 179–205. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17989-2_8.
Full textUkil, Abhisek. "Support Vector Machine." In Power Systems, 161–226. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73170-2_4.
Full textSuzuki, Joe. "Support Vector Machine." In Statistical Learning with Math and R, 171–92. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7568-6_9.
Full textYu, Hwanjo. "Support Vector Machine." In Encyclopedia of Database Systems, 1–4. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_557-2.
Full textYu, Hwanjo. "Support Vector Machine." In Encyclopedia of Database Systems, 2890–92. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_557.
Full textAdankon, Mathias M., and Mohamed Cheriet. "Support Vector Machine." In Encyclopedia of Biometrics, 1–9. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27733-7_299-3.
Full textAberham, Jana, and Fabrizio Kuruc. "Support Vector Machine." In Wie Maschinen lernen, 95–103. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-26763-6_13.
Full textEl Morr, Christo, Manar Jammal, Hossam Ali-Hassan, and Walid El-Hallak. "Support Vector Machine." In International Series in Operations Research & Management Science, 385–411. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16990-8_13.
Full textLi, Hang. "Support Vector Machine." In Machine Learning Methods, 127–77. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3917-6_7.
Full textConference papers on the topic "Support Vector Machine"
Qi, Xiaomin, Sergei Silvestrov, and Talat Nazir. "Data classification with support vector machine and generalized support vector machine." In ICNPAA 2016 WORLD CONGRESS: 11th International Conference on Mathematical Problems in Engineering, Aerospace and Sciences. Author(s), 2017. http://dx.doi.org/10.1063/1.4972718.
Full textLe, Trung, Dat Tran, Wanli Ma, Thien Pham, Phuong Duong, and Minh Nguyen. "Robust Support Vector Machine." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889587.
Full textLv, Xutao. "Randomized Support Vector Forest." In British Machine Vision Conference 2014. British Machine Vision Association, 2014. http://dx.doi.org/10.5244/c.28.61.
Full textQilong, Zhang, Shan Ganlin, and Duan Xiusheng. "Weighted Support Vector Machine Based Clustering Vector." In 2008 International Conference on Computer Science and Software Engineering. IEEE, 2008. http://dx.doi.org/10.1109/csse.2008.1454.
Full textYao, Chih-Chia, and Pao-Ta Yu. "Effective Training of Support Vector Machines using Extractive Support Vector Algorithm." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370441.
Full textKong, Bo, and Hong-wei Wang. "Reduced Support Vector Machine Based on Margin Vectors." In 2010 International Conference on Computational Intelligence and Software Engineering (CiSE). IEEE, 2010. http://dx.doi.org/10.1109/cise.2010.5677026.
Full textXu Zhou, Shu-Xia Lu, Li-Sha Hu, and Meng Zhang. "Imbalanced extreme support vector machine." In 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6358971.
Full textFung, Glenn, and Olvi L. Mangasarian. "Proximal support vector machine classifiers." In the seventh ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/502512.502527.
Full textKuo, R. J., and C. M. Chen. "Evolutionary-based support vector machine." In 2011 IEEE MTT-S International Microwave Workshop Series on Innovative Wireless Power Transmission: Technologies, Systems, and Applications (IMWS 2011). IEEE, 2011. http://dx.doi.org/10.1109/imws.2011.6114985.
Full textLi, WanLing, Peng Chen, and Xiangjun Song. "Improved Weighted Support Vector Machine." In 2016 5th International Conference on Advanced Materials and Computer Science (ICAMCS 2016). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/icamcs-16.2016.4.
Full textReports on the topic "Support Vector Machine"
Gertz, E. M., and J. D. Griffin. Support vector machine classifiers for large data sets. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/881587.
Full textAlali, Ali. Application of Support Vector Machine in Predicting the Market's Monthly Trend Direction. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1495.
Full textO'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.), November 2022. http://dx.doi.org/10.21079/11681/45842.
Full textArun, 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, October 2019. http://dx.doi.org/10.7546/crabs.2019.10.12.
Full textLiu, 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.
Full textQi, Yuan. Learning Algorithms for Audio and Video Processing: Independent Component Analysis and Support Vector Machine Based Approaches. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada458739.
Full textLuo, 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, January 2018. http://dx.doi.org/10.7546/crabs.2018.01.16.
Full textLuo, 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, January 2018. http://dx.doi.org/10.7546/grabs2018.1.16.
Full textLasko, Kristofer, and Elena Sava. Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42402.
Full textAlwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, December 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.
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