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Artykuły w czasopismach na temat "Support Vector Machine"
Xia, Tian. "Support Vector Machine Based Educational Resources Classification". International Journal of Information and Education Technology 6, nr 11 (2016): 880–83. http://dx.doi.org/10.7763/ijiet.2016.v6.809.
Pełny tekst źródłaBE, R. Aruna Sankari. "Cervical Cancer Detection Using Support Vector Machine". International journal of Emerging Trends in Science and Technology 04, nr 03 (31.03.2017): 5033–38. http://dx.doi.org/10.18535/ijetst/v4i3.08.
Pełny tekst źródłaHeo, Gyeong-Yong, i Seong-Hoon Kim. "Context-Aware Fusion with Support Vector Machine". Journal of the Korea Society of Computer and Information 19, nr 6 (30.06.2014): 19–26. http://dx.doi.org/10.9708/jksci.2014.19.6.019.
Pełny tekst źródłaHuimin, Yao. "Research on Parallel Support Vector Machine Based on Spark Big Data Platform". Scientific Programming 2021 (17.12.2021): 1–9. http://dx.doi.org/10.1155/2021/7998417.
Pełny tekst źródłaV., Dr Padmanabha Reddy. "Human Cognitive State classification using Support Vector Machine". Journal of Advanced Research in Dynamical and Control Systems 12, nr 01-Special Issue (13.02.2020): 46–54. http://dx.doi.org/10.5373/jardcs/v12sp1/20201045.
Pełny tekst źródłaJung, Kang-Mo. "Robust Algorithm for Multiclass Weighted Support Vector Machine". SIJ Transactions on Advances in Space Research & Earth Exploration 4, nr 3 (10.06.2016): 1–5. http://dx.doi.org/10.9756/sijasree/v4i3/0203430402.
Pełny tekst źródłaDhaifallah, Mujahed Al, i K. S. Nisar. "Support Vector Machine Identification of Subspace Hammerstein Models". International Journal of Computer Theory and Engineering 7, nr 1 (luty 2014): 9–15. http://dx.doi.org/10.7763/ijcte.2015.v7.922.
Pełny tekst źródłaYANG, Zhi-Min, Yuan-Hai SHAO i Jing LIANG. "Unascertained Support Vector Machine". Acta Automatica Sinica 39, nr 6 (25.03.2014): 895–901. http://dx.doi.org/10.3724/sp.j.1004.2013.00895.
Pełny tekst źródłaZhang, L., W. Zhou i L. Jiao. "Wavelet Support Vector Machine". IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, nr 1 (luty 2004): 34–39. http://dx.doi.org/10.1109/tsmcb.2003.811113.
Pełny tekst źródłaNavia-Vázquez, A., i E. Parrado-Hernández. "Support vector machine interpretation". Neurocomputing 69, nr 13-15 (sierpień 2006): 1754–59. http://dx.doi.org/10.1016/j.neucom.2005.12.118.
Pełny tekst źródłaRozprawy doktorskie na temat "Support Vector Machine"
Cardamone, Dario. "Support Vector Machine a Machine Learning Algorithm". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.
Znajdź pełny tekst źródłaMcChesney, Charlie. "External Support Vector Machine Clustering". ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/409.
Pełny tekst źródłaArmond, Kenneth C. Jr. "Distributed Support Vector Machine Learning". ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/711.
Pełny tekst źródłaZigic, Ljiljana. "Direct L2 Support Vector Machine". VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4274.
Pełny tekst źródłaPark, 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.
Pełny tekst źródłaTsang, Wai-Hung. "Scaling up support vector machines /". View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20TSANG.
Pełny tekst źródłaPerez, 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.
Pełny tekst źródłaWen, Tong 1970. "Support Vector Machine algorithms : analysis and applications". Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8404.
Pełny tekst źródłaIncludes 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.
Pełny tekst źródłaTitle 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.
Pełny tekst źródłaKsiążki na temat "Support Vector Machine"
Andreas, Christmann, red. Support vector machines. New York: Springer, 2008.
Znajdź pełny tekst źródłaCampbell, Colin. Learning with support vector machines. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.
Znajdź pełny tekst źródłaJoachim, Diederich, red. Rule extraction from support vector machines. Berlin: Springer, 2008.
Znajdź pełny tekst źródłaBoyle, Brandon H. Support vector machines: Data analysis, machine learning, and applications. Hauppauge, N.Y: Nova Science Publishers, 2011.
Znajdź pełny tekst źródłaHamel, Lutz. Knowledge discovery with support vector machines. Hoboken, N.J: John Wiley & Sons, 2009.
Znajdź pełny tekst źródłamissing], [name. Least squares support vector machines. Singapore: World Scientific, 2002.
Znajdź pełny tekst źródłaErtekin, Şeyda. Algorithms for efficient learning systems: Online and active learning approaches. Saarbrücken: VDM Verlag Dr. Müller, 2009.
Znajdź pełny tekst źródłaSupport vector machines for pattern classification. Wyd. 2. London: Springer, 2010.
Znajdź pełny tekst źródłaJoachims, Thorsten. Learning to classify text using support vector machines. Boston: Kluwer Academic Publishers, 2002.
Znajdź pełny tekst źródłaK, Suykens Johan A., Signoretto Marco i Argyriou Andreas, red. Regularization, optimization, kernels, and support vector machines. Boca Raton: Taylor & Francis, 2014.
Znajdź pełny tekst źródłaCzęści książek na temat "Support Vector Machine"
Zhou, Zhi-Hua. "Support Vector Machine". W Machine Learning, 129–53. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1967-3_6.
Pełny tekst źródłaZhang, Dengsheng. "Support Vector Machine". W Texts in Computer Science, 179–205. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17989-2_8.
Pełny tekst źródłaUkil, Abhisek. "Support Vector Machine". W Power Systems, 161–226. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73170-2_4.
Pełny tekst źródłaSuzuki, Joe. "Support Vector Machine". W Statistical Learning with Math and R, 171–92. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7568-6_9.
Pełny tekst źródłaYu, Hwanjo. "Support Vector Machine". W 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.
Pełny tekst źródłaYu, Hwanjo. "Support Vector Machine". W Encyclopedia of Database Systems, 2890–92. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_557.
Pełny tekst źródłaAdankon, Mathias M., i Mohamed Cheriet. "Support Vector Machine". W Encyclopedia of Biometrics, 1–9. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27733-7_299-3.
Pełny tekst źródłaAberham, Jana, i Fabrizio Kuruc. "Support Vector Machine". W Wie Maschinen lernen, 95–103. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-26763-6_13.
Pełny tekst źródłaEl Morr, Christo, Manar Jammal, Hossam Ali-Hassan i Walid El-Hallak. "Support Vector Machine". W 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.
Pełny tekst źródłaLi, Hang. "Support Vector Machine". W Machine Learning Methods, 127–77. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3917-6_7.
Pełny tekst źródłaStreszczenia konferencji na temat "Support Vector Machine"
Qi, Xiaomin, Sergei Silvestrov i Talat Nazir. "Data classification with support vector machine and generalized support vector machine". W 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.
Pełny tekst źródłaLe, Trung, Dat Tran, Wanli Ma, Thien Pham, Phuong Duong i Minh Nguyen. "Robust Support Vector Machine". W 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889587.
Pełny tekst źródłaLv, Xutao. "Randomized Support Vector Forest". W British Machine Vision Conference 2014. British Machine Vision Association, 2014. http://dx.doi.org/10.5244/c.28.61.
Pełny tekst źródłaQilong, Zhang, Shan Ganlin i Duan Xiusheng. "Weighted Support Vector Machine Based Clustering Vector". W 2008 International Conference on Computer Science and Software Engineering. IEEE, 2008. http://dx.doi.org/10.1109/csse.2008.1454.
Pełny tekst źródłaYao, Chih-Chia, i Pao-Ta Yu. "Effective Training of Support Vector Machines using Extractive Support Vector Algorithm". W 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370441.
Pełny tekst źródłaKong, Bo, i Hong-wei Wang. "Reduced Support Vector Machine Based on Margin Vectors". W 2010 International Conference on Computational Intelligence and Software Engineering (CiSE). IEEE, 2010. http://dx.doi.org/10.1109/cise.2010.5677026.
Pełny tekst źródłaXu Zhou, Shu-Xia Lu, Li-Sha Hu i Meng Zhang. "Imbalanced extreme support vector machine". W 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6358971.
Pełny tekst źródłaFung, Glenn, i Olvi L. Mangasarian. "Proximal support vector machine classifiers". W the seventh ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/502512.502527.
Pełny tekst źródłaKuo, R. J., i C. M. Chen. "Evolutionary-based support vector machine". W 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.
Pełny tekst źródłaLi, WanLing, Peng Chen i Xiangjun Song. "Improved Weighted Support Vector Machine". W 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.
Pełny tekst źródłaRaporty organizacyjne na temat "Support Vector Machine"
Gertz, E. M., i J. D. Griffin. Support vector machine classifiers for large data sets. Office of Scientific and Technical Information (OSTI), styczeń 2006. http://dx.doi.org/10.2172/881587.
Pełny tekst źródłaAlali, Ali. Application of Support Vector Machine in Predicting the Market's Monthly Trend Direction. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.1495.
Pełny tekst źródłaO'Neill, Francis, Kristofer Lasko i 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.), listopad 2022. http://dx.doi.org/10.21079/11681/45842.
Pełny tekst źródłaArun, Ramaiah, i 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, październik 2019. http://dx.doi.org/10.7546/crabs.2019.10.12.
Pełny tekst źródłaLiu, 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.
Pełny tekst źródłaQi, Yuan. Learning Algorithms for Audio and Video Processing: Independent Component Analysis and Support Vector Machine Based Approaches. Fort Belvoir, VA: Defense Technical Information Center, sierpień 2000. http://dx.doi.org/10.21236/ada458739.
Pełny tekst źródłaLuo, Yuzhou, Rui Wang, Zhongwei Jiang i 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, styczeń 2018. http://dx.doi.org/10.7546/crabs.2018.01.16.
Pełny tekst źródłaLuo, Yuzhou, Rui Wang, Zhongwei Jiang i 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, styczeń 2018. http://dx.doi.org/10.7546/grabs2018.1.16.
Pełny tekst źródłaLasko, Kristofer, i 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.), listopad 2021. http://dx.doi.org/10.21079/11681/42402.
Pełny tekst źródłaAlwan, Iktimal, Dennis D. Spencer i Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, grudzień 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.
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