Relevant bibliographies by topics / Support vector machines

Academic literature on the topic 'Support vector machines'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Support vector machines"

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Guenther, Nick, and Matthias Schonlau. "Support Vector Machines." Stata Journal: Promoting communications on statistics and Stata 16, no. 4 (December 2016): 917–37. http://dx.doi.org/10.1177/1536867x1601600407.

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Support vector machines are statistical- and machine-learning techniques with the primary goal of prediction. They can be applied to continuous, binary, and categorical outcomes analogous to Gaussian, logistic, and multinomial regression. We introduce a new command for this purpose, svmachines. This package is a thin wrapper for the widely deployed libsvm (Chang and Lin, 2011, ACM Transactions on Intelligent Systems and Technology 2(3): Article 27). We illustrate svmachines with two examples.
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Hearst, M. A., S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf. "Support vector machines." IEEE Intelligent Systems and their Applications 13, no. 4 (July 1998): 18–28. http://dx.doi.org/10.1109/5254.708428.

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Bennett, Kristin P., and Colin Campbell. "Support vector machines." ACM SIGKDD Explorations Newsletter 2, no. 2 (December 2000): 1–13. http://dx.doi.org/10.1145/380995.380999.

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Mammone, Alessia, Marco Turchi, and Nello Cristianini. "Support vector machines." Wiley Interdisciplinary Reviews: Computational Statistics 1, no. 3 (November 2009): 283–89. http://dx.doi.org/10.1002/wics.49.

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Valkenborg, Dirk, Axel-Jan Rousseau, Melvin Geubbelmans, and Tomasz Burzykowski. "Support vector machines." American Journal of Orthodontics and Dentofacial Orthopedics 164, no. 5 (November 2023): 754–57. http://dx.doi.org/10.1016/j.ajodo.2023.08.003.

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Giustolisi, Orazio. "Using a multi-objective genetic algorithm for SVM construction." Journal of Hydroinformatics 8, no. 2 (March 1, 2006): 125–39. http://dx.doi.org/10.2166/hydro.2006.016b.

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Support Vector Machines are kernel machines useful for classification and regression problems. In this paper, they are used for non-linear regression of environmental data. From a structural point of view, Support Vector Machines are particular Artificial Neural Networks and their training paradigm has some positive implications. In fact, the original training approach is useful to overcome the curse of dimensionality and too strict assumptions on statistics of the errors in data. Support Vector Machines and Radial Basis Function Regularised Networks are presented within a common structural framework for non-linear regression in order to emphasise the training strategy for support vector machines and to better explain the multi-objective approach in support vector machines' construction. A support vector machine's performance depends on the kernel parameter, input selection and ε-tube optimal dimension. These will be used as decision variables for the evolutionary strategy based on a Genetic Algorithm, which exhibits the number of support vectors, for the capacity of machine, and the fitness to a validation subset, for the model accuracy in mapping the underlying physical phenomena, as objective functions. The strategy is tested on a case study dealing with groundwater modelling, based on time series (past measured rainfalls and levels) for level predictions at variable time horizons.
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Heo, Gyeongyong. "Context Dependent Fusion with Support Vector Machines." Journal of the Korea Society of Computer and Information 18, no. 7 (July 31, 2013): 37–45. http://dx.doi.org/10.9708/jksci.2013.18.7.037.

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Fearn, Tom. "Support Vector Machines I: The Support Vector Classifier." NIR news 15, no. 5 (October 2004): 14–15. http://dx.doi.org/10.1255/nirn.788.

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Goyal, Vishal, and Aasheesh Shukla. "Content based Image Retrieval Using Support Vector Machines." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 625–32. http://dx.doi.org/10.5373/jardcs/v11sp10/20192851.

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REN, Shuang-Qiao, De-Gui YANG, Xiang LI, and Zhao-Wen ZHUANG. "Piecewise Support Vector Machines." Chinese Journal of Computers 32, no. 1 (July 29, 2009): 77–85. http://dx.doi.org/10.3724/sp.j.1016.2009.00077.

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Dissertations / Theses on the topic "Support vector machines"

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Meyer, David, Friedrich Leisch, and Kurt Hornik. "Benchmarking Support Vector Machines." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2002. http://epub.wu.ac.at/1578/1/document.pdf.

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Support Vector Machines (SVMs) are rarely benchmarked against other classification or regression methods. We compare a popular SVM implementation (libsvm) to 16 classification methods and 9 regression methods-all accessible through the software R-by the means of standard performance measures (classification error and mean squared error) which are also analyzed by the means of bias-variance decompositions. SVMs showed mostly good performances both on classification and regression tasks, but other methods proved to be very competitive.
Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Möller, Manuel. "Spamerkennung mit Support Vector Machines." Thesis, Universitätsbibliothek Chemnitz, 2005. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200500580.

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Diese Arbeit zeigt ausgehend von einer Darstellung der theoretischen Grundlagen automatischer Textklassifikation, dass die aus der Statistical Learning Theory stammenden Support Vector Machines geeignet sind, zu einer präziseren Erkennung unerwünschter E-Mail-Werbung beizutragen. In einer Testumgebung mit einem Corpus von 20 000 E-Mails wurden Testläufe verschiedene Parameter der Vorverarbeitung und der Support Vector Machine automatisch evaluiert und grafisch visualisiert. Aufbauend darauf wird eine Erweiterung für die Open-Source-Software SpamAssassin beschrieben, die die vorhandenen Klassifikationsmechanismen um eine Klassifikation per Support Vector Machine erweitert.
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Gao, Yunchuan. "Multi-category support vector machines." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2006. http://proquest.umi.com/login?COPT=REJTPTU0NWQmSU5UPTAmVkVSPTI=&clientId=3739.

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Tsang, Wai-Hung. "Scaling up support vector machines /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20TSANG.

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Karatzoglou, Alexandros, David Meyer, and Kurt Hornik. "Support Vector Machines in R." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/1500/1/document.pdf.

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Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language. Currently four R packages contain SVM related software. The purpose of this paper is to present and compare these implementations. (author's abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Hornik, Kurt, David Meyer, and Alexandros Karatzoglou. "Support Vector Machines in R." American Statistical Association, 2006. http://epub.wu.ac.at/3986/1/supportvector.pdf.

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Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language. Currently four R packages contain SVM related software. The purpose of this paper is to present and compare these implementations. (authors' abstract)
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Möller, Manuel. "Spamerkennung mit Support Vector Machines." [S.l. : s.n.], 2005. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB12046020.

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Merat, Sepehr. "Clustering Via Supervised Support Vector Machines." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/857.

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An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labeled. Once this initialization step is complete, the SVM confidence parameters for classification on each of the training instances can be accessed. The lowest confidence data (e.g., the worst of the mislabeled data) then has its labels switched to the other class label. The SVM is then re-run on the data set (with partly re-labeled data). The repetition of the above process improves the separability until there is no misclassification. Variations on this type of clustering approach are shown.
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Youn, Eun Seog. "Feature selection in support vector machines." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE1000171.

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Thesis (M.S.)--University of Florida, 2002.
Title from title page of source document. Document formatted into pages; contains x, 50 p.; also contains graphics. Includes vita. Includes bibliographical references.
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Ganapathiraju, Aravind. "Support Vector Machines for Speech Recognition." MSSTATE, 2002. http://sun.library.msstate.edu/ETD-db/theses/available/etd-02202002-111027/.

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Hidden Markov models (HMM) with Gaussian mixture observation densities are the dominant approach in speech recognition. These systems typically use a representational model for acoustic modeling which can often be prone to overfitting and does not translate to improved discrimination. We propose a new paradigm centered on principles of structural risk minimization using a discriminative framework for speech recognition based on support vector machines (SVMs). SVMs have the ability to simultaneously optimize the representational and discriminative ability of the acoustic classifiers. We have developed the first SVM-based large vocabulary speech recognition system that improves performance over traditional HMM-based systems. This hybrid system achieves a state-of-the-art word error rate of 10.6% on a continuous alphadigit task ? a 10% improvement relative to an HMM system. On SWITCHBOARD, a large vocabulary task, the system improves performance over a traditional HMM system from 41.6% word error rate to 40.6%. This dissertation discusses several practical issues that arise when SVMs are incorporated into the hybrid system.
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Books on the topic "Support vector machines"

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Andreas, Christmann, ed. Support vector machines. New York: Springer, 2008.

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Campbell, Colin. Learning with support vector machines. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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Support vector machines applications. Cham [Switzerland]: Springer, 2014.

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Jayadeva, Reshma Khemchandani, and Suresh Chandra. Twin Support Vector Machines. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46186-1.

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Ma, Yunqian, and Guodong Guo, eds. Support Vector Machines Applications. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02300-7.

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Joachim, Diederich, ed. Rule extraction from support vector machines. Berlin: Springer, 2008.

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Campbell, Colin, and Yiming Ying. Learning with Support Vector Machines. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01552-6.

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Murty, M. N., and Rashmi Raghava. Support Vector Machines and Perceptrons. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41063-0.

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missing], [name. Least squares support vector machines. Singapore: World Scientific, 2002.

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Hamel, Lutz. Knowledge discovery with support vector machines. Hoboken, N.J: John Wiley & Sons, 2009.

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Book chapters on the topic "Support vector machines"

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Pal, Mahesh, and Pakorn Watanachaturaporn. "Support Vector Machines." In Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data, 133–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05605-9_6.

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Du, Ke-Lin, and M. N. S. Swamy. "Support Vector Machines." In Neural Networks and Statistical Learning, 469–524. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5571-3_16.

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Abe, Shigeo. "Support Vector Machines." In Pattern Classification, 47–61. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0285-4_3.

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Li, Li. "Support Vector Machines." In Springer Optimization and Its Applications, 17–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46356-7_2.

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Murty, M. Narasimha, and V. Susheela Devi. "Support Vector Machines." In Undergraduate Topics in Computer Science, 147–87. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-495-1_7.

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Joachims, Thorsten. "Support Vector Machines." In Learning to Classify Text Using Support Vector Machines, 35–44. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0907-3_3.

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Du, Ke-Lin, and M. N. S. Swamy. "Support Vector Machines." In Neural Networks and Statistical Learning, 593–644. London: Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-7452-3_21.

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Pérez Castaño, Arnaldo. "Support Vector Machines." In Practical Artificial Intelligence, 315–65. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3357-3_9.

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Cristianini, Nello, and Elisa Ricci. "Support Vector Machines." In Encyclopedia of Algorithms, 2170–74. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_415.

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Cristianini, Nello, and Elisa Ricci. "Support Vector Machines." In Encyclopedia of Algorithms, 1–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27848-8_415-2.

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Conference papers on the topic "Support vector machines"

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Wu, Shan-Hung, Keng-Pei Lin, Chung-Min Chen, and Ming-Syan Chen. "Asymmetric support vector machines." In the 14th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1401890.1401980.

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Lee, Gyemin, and Clayton Scott. "Nested support vector machines." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518027.

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Huang, Kaizhu, Haochuan Jiang, and Xu-Yao Zhang. "Field support vector machines." In IML 2017: International Conference on Internet of Things and Machine Learning. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3109761.3158392.

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Davenport, Mark A., Richard G. Baraniuk, and Clayton D. Scott. "Minimax Support Vector Machines." In 2007 IEEE/SP 14th Workshop on Statistical Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/ssp.2007.4301335.

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Li, Minqiang, Fuzan Chen, and Jisong Kou. "Candidate Vectors Selection for Training Support Vector Machines." In Third International Conference on Natural Computation (ICNC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnc.2007.292.

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Mohapatra, Shubham. "Segmentation using Support Vector Machines." In 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP). IEEE, 2019. http://dx.doi.org/10.1109/icaccp.2019.8882941.

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Shohdy, Sameh, Abhinav Vishnu, and Gagan Agrawal. "Fault Tolerant Support Vector Machines." In 2016 45th International Conference on Parallel Processing (ICPP). IEEE, 2016. http://dx.doi.org/10.1109/icpp.2016.75.

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Kudo, Taku, and Yuji Matsumoto. "Chunking with support vector machines." In Second meeting of the North American Chapter of the Association for Computational Linguistics. Morristown, NJ, USA: Association for Computational Linguistics, 2001. http://dx.doi.org/10.3115/1073336.1073361.

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Ji, You, and Shiliang Sun. "Multitask Multiclass Support Vector Machines." In 2011 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2011. http://dx.doi.org/10.1109/icdmw.2011.126.

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Cevikalp, Hakan, and Merve Elmas. "Robust transductive support vector machines." In 2016 24th Signal Processing and Communication Application Conference (SIU). IEEE, 2016. http://dx.doi.org/10.1109/siu.2016.7495907.

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Reports on the topic "Support vector machines"

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Mangasarian, Olvi L. Data Mining via Generalized Support Vector Machines. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada414231.

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Phillips, P. Jonathon. Support vector machines applied to face recognition. Gaithersburg, MD: National Institute of Standards and Technology, 1998. http://dx.doi.org/10.6028/nist.ir.6241.

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Zhao, Ying, and George Karypis. Prediction of Contact Maps Using Support Vector Machines. Fort Belvoir, VA: Defense Technical Information Center, December 2002. http://dx.doi.org/10.21236/ada439501.

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Baras, John S., and Maben Rabi. Intrusion Detection With Support Vector Machines and Generative Models. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada439783.

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Tabata, Akihisa, and Yoshio Aoki. Application of Support Vector Machines to Structural Health Monitoring. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0102.

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Walsh, Timothy, Wilkins Aquino, Andrew Kurzawski, Cameron McCormick, Clay Sanders, Chandler Smith, and Benjamin Treweek. Support Vector Machines for Estimating Decision Boundaries with Numerical Simulations. Office of Scientific and Technical Information (OSTI), August 2022. http://dx.doi.org/10.2172/1882043.

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Meza, Juan C., and Mark Woods. A Numerical Comparison of Rule Ensemble Methods and Support Vector Machines. Office of Scientific and Technical Information (OSTI), December 2009. http://dx.doi.org/10.2172/981337.

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Heisele, Bernd, Purdy Ho, and Tomaso Poggio. Face Recognition with Support Vector Machines: Global versus Component-Based Approach. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada459707.

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Singam, Amitesh Kumar, and Venkat Raj Reddy Pashike. Low complex blind video quality predictor based on support vector machines. Peeref, March 2023. http://dx.doi.org/10.54985/peeref.2303p3579957.

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Umbreen, Fariha, Faizan Ahmed, and Muhammad Faisal Iqbal. On Convergence Rate of a Convex Relaxation of Semisupervised Support Vector Machines. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, May 2019. http://dx.doi.org/10.7546/crabs.2019.05.02.

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