Academic literature on the topic 'Structured Support Vector Machine'
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Journal articles on the topic "Structured Support Vector Machine"
Kim, Kyoungok, and Daewon Lee. "Inductive manifold learning using structured support vector machine." Pattern Recognition 47, no. 1 (January 2014): 470–79. http://dx.doi.org/10.1016/j.patcog.2013.07.011.
Full textZhang, Shunli, Yao Sui, Sicong Zhao, and Li Zhang. "Graph-Regularized Structured Support Vector Machine for Object Tracking." IEEE Transactions on Circuits and Systems for Video Technology 27, no. 6 (June 2017): 1249–62. http://dx.doi.org/10.1109/tcsvt.2015.2513659.
Full textSharma, Manoj Kumar, and Vijaypal Singh Dhaka. "Segmentation of handwritten words using structured support vector machine." Pattern Analysis and Applications 23, no. 3 (September 16, 2019): 1355–67. http://dx.doi.org/10.1007/s10044-019-00843-x.
Full textCuong, Nguyen The, and Huynh The Phung. "WEIGHTED STRUCTURAL SUPPORT VECTOR MACHINE." Journal of Computer Science and Cybernetics 37, no. 1 (March 29, 2021): 43–56. http://dx.doi.org/10.15625/1813-9663/37/1/15396.
Full textJeon, Yoondeok, Jiwoo Oh, Seungjae Lim, Yewon Choi, Sungmoon Kim, and Taeseon Yoon. "Analysis of Structural Relationship between Immunodeficiency Viruses Using Support Vector Machine." International Journal of Computer Theory and Engineering 7, no. 1 (February 2014): 46–50. http://dx.doi.org/10.7763/ijcte.2015.v7.928.
Full textQu, Qiang, Ming Qi Chang, Lei Xu, Yue Wang, and Shao Hua Lu. "Support Vector Machine-Based Aqueduct Safety Assessment." Advanced Materials Research 368-373 (October 2011): 531–36. http://dx.doi.org/10.4028/www.scientific.net/amr.368-373.531.
Full textZHANG, LI, WEI-DA ZHOU, TIAN-TIAN SU, and LI-CHENG JIAO. "DECISION TREE SUPPORT VECTOR MACHINE." International Journal on Artificial Intelligence Tools 16, no. 01 (February 2007): 1–15. http://dx.doi.org/10.1142/s0218213007003163.
Full textYamamoto, Maeri, Epifanio Bagarinao, Itaru Kushima, Tsutomu Takahashi, Daiki Sasabayashi, Toshiya Inada, Michio Suzuki, Tetsuya Iidaka, and Norio Ozaki. "Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites." PLOS ONE 15, no. 11 (November 24, 2020): e0239615. http://dx.doi.org/10.1371/journal.pone.0239615.
Full textHou, Qiuling, Ling Zhen, Naiyang Deng, and Ling Jing. "Novel Grouping Method-based support vector machine plus for structured data." Neurocomputing 211 (October 2016): 191–201. http://dx.doi.org/10.1016/j.neucom.2016.03.086.
Full textHao, Pei-Yi, Jung-Hsien Chiang, and Yen-Hsiu Lin. "A new maximal-margin spherical-structured multi-class support vector machine." Applied Intelligence 30, no. 2 (October 18, 2007): 98–111. http://dx.doi.org/10.1007/s10489-007-0101-z.
Full textDissertations / Theses on the topic "Structured Support Vector Machine"
Tsochantaridis, Ioannis. "Support vector machine learning for interdependent and structured output spaces /." View online version; access limited to Brown University users, 2005. http://wwwlib.umi.com/dissertations/fullcit/3174684.
Full textZhang, Shi-Xiong. "Structured support vector machines for speech recognition." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708040.
Full textSharma, Siddharth. "Application of Support Vector Machines for Damage Detection in Structures." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/8.
Full textZhong, Wei. "Clustering System and Clustering Support Vector Machine for Local Protein Structure Prediction." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/7.
Full textReyaz-Ahmed, Anjum B. "Protein Secondary Structure Prediction Using Support Vector Machines, Nueral Networks and Genetic Algorithms." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_theses/43.
Full textGuimarães, Ana Paula Alves [UNESP]. "Utilização do algoritmo de aprendizado de máquinas para monitoramento de falhas em estruturas inteligentes." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/148718.
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O monitoramento da condição estrutural é uma área que vem sendo bastante estudada por permitir a construção de sistemas que possuem a capacidade de identificar um determinado dano em seu estágio inicial, podendo assim evitar sérios prejuízos futuros. O ideal seria que estes sistemas tivessem o mínimo de interferência humana. Sistemas que abordam o conceito de aprendizagem têm a capacidade de serem autômatos. Acredita-se que por possuírem estas propriedades, os algoritmos de aprendizagem de máquina sejam uma excelente opção para realizar as etapas de identificação, localização e avaliação de um dano, com capacidade de obter resultados extremamente precisos e com taxas mínimas de erros. Este trabalho tem como foco principal utilizar o algoritmo support vector machine no auxílio do monitoramento da condição de estruturas e, com isto, obter melhor exatidão na identificação da presença ou ausência do dano, diminuindo as taxas de erros através das abordagens da aprendizagem de máquina, possibilitando, assim, um monitoramento inteligente e eficiente. Foi utilizada a biblioteca LibSVM para análise e validação da proposta. Desta forma, foi possível realizar o treinamento e classificação dos dados promovendo a identificação dos danos e posteriormente, empregando as predições efetuadas pelo algoritmo, foi possível determinar a localização dos danos na estrutura. Os resultados de identificação e localização dos danos foram bastante satisfatórios.
Structural health monitoring (SHM) is an area that has been extensively studied for allowing the construction of systems that have the ability to identify damages at an early stage, thus being able to avoid serious future losses. Ideally, these systems have the minimum of human interference. Systems that address the concept of learning have the ability to be autonomous. It is believed that by having these properties, the machine learning algorithms are an excellent choice to perform the steps of identifying, locating and assessing damage with ability to obtain highly accurate results with minimum error rates. This work is mainly focused on using support vector machine algorithm for monitoring structural condition and, thus, get better accuracy in identifying the presence or absence of damage, reducing error rates through the approaches of machine learning. It allows an intelligent and efficient monitoring system. LIBSVM library was used for analysing and validation of the proposed approach. Thus, it was feasible to conduct training and classification of data promoting the identification of damages. It was also possible to locate the damages in the structure. The results of identification and location of the damage was quite satisfactory.
Guimarães, Ana Paula Alves. "Utilização do algoritmo de aprendizado de máquinas para monitoramento de falhas em estruturas inteligentes /." Ilha Solteira, 2016. http://hdl.handle.net/11449/148718.
Full textResumo: Structural health monitoring (SHM) is an area that has been extensively studied for allowing the construction of systems that have the ability to identify damages at an early stage, thus being able to avoid serious future losses. Ideally, these systems have the minimum of human interference. Systems that address the concept of learning have the ability to be autonomous. It is believed that by having these properties, the machine learning algorithms are an excellent choice to perform the steps of identifying, locating and assessing damage with ability to obtain highly accurate results with minimum error rates. This work is mainly focused on using support vector machine algorithm for monitoring structural condition and, thus, get better accuracy in identifying the presence or absence of damage, reducing error rates through the approaches of machine learning. It allows an intelligent and efficient monitoring system. LIBSVM library was used for analysing and validation of the proposed approach. Thus, it was feasible to conduct training and classification of data promoting the identification of damages. It was also possible to locate the damages in the structure. The results of identification and location of the damage was quite satisfactory.
Mestre
Dalvi, Aditi. "Performance of One-class Support Vector Machine (SVM) in Detection of Anomalies in the Bridge Data." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin150478019017791.
Full textAltun, Gulsah. "Machine Learning and Graph Theory Approaches for Classification and Prediction of Protein Structure." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/cs_diss/31.
Full textUziela, Karolis. "Protein Model Quality Assessment : A Machine Learning Approach." Doctoral thesis, Stockholms universitet, Institutionen för biokemi och biofysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-137695.
Full textAt the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.
Books on the topic "Structured Support Vector Machine"
Andreas, Christmann, ed. Support vector machines. New York: Springer, 2008.
Find full textJoachims, Thorsten. Learning to Classify Text Using Support Vector Machines. Boston, MA: Springer US, 2002.
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 textname, No. Least squares support vector machines. Singapore: World Scientific, 2002.
Find full textJoachim, Diederich, ed. Rule extraction from support vector machines. Berlin: Springer, 2008.
Find full textHamel, Lutz. Knowledge discovery with support vector machines. Hoboken, N.J: John Wiley & Sons, 2009.
Find full textBernhard, Schölkopf, Burges Christopher J. C, and Smola Alexander J, eds. Advances in kernel methods: Support vector learning. Cambridge, Mass: MIT Press, 1999.
Find full textBoyle, Brandon H. Support vector machines: Data analysis, machine learning, and applications. Hauppauge, N.Y: Nova Science Publishers, 2011.
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 textSupport vector machines for pattern classification. 2nd ed. London: Springer, 2010.
Find full textBook chapters on the topic "Structured Support Vector Machine"
Schiegg, Martin, Ferran Diego, and Fred A. Hamprecht. "Learning Diverse Models: The Coulomb Structured Support Vector Machine." In Computer Vision – ECCV 2016, 585–99. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46487-9_36.
Full textJoachims, Thorsten. "Structured Output Prediction with Support Vector Machines." In Lecture Notes in Computer Science, 1–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11815921_1.
Full textHua, Kai-Lung, Irawati Nurmala Sari, and Mei-Chen Yeh. "Human Pose Tracking Using Online Latent Structured Support Vector Machine." In MultiMedia Modeling, 626–37. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51811-4_51.
Full textHaas, Rainer, and Kurt Pichler. "Fault Diagnosis in a Hydraulic Circuit Using a Support Vector Machine Trained by a Digital Twin." In Advanced Structured Materials, 47–60. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79325-8_5.
Full textSeveryn, Aliaksei, and Alessandro Moschitti. "Fast Support Vector Machines for Structural Kernels." In Machine Learning and Knowledge Discovery in Databases, 175–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23808-6_12.
Full textSeveryn, Aliaksei, and Alessandro Moschitti. "Large-Scale Support Vector Learning with Structural Kernels." In Machine Learning and Knowledge Discovery in Databases, 229–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15939-8_15.
Full textLe, Hieu Quang, and Stefan Conrad. "Classifying Structured Web Sources Using Support Vector Machine and Aggressive Feature Selection." In Lecture Notes in Business Information Processing, 270–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12436-5_20.
Full textPan, Hong, Mohsen Azimi, Guoqing Gui, Fei Yan, and Zhibin Lin. "Vibration-Based Support Vector Machine for Structural Health Monitoring." In Lecture Notes in Civil Engineering, 167–78. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67443-8_14.
Full textSchwenker, Friedhelm, and Günther Palm. "Tree-Structured Support Vector Machines for Multi-class Pattern Recognition." In Multiple Classifier Systems, 409–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48219-9_41.
Full textZhang, Zhou Suo, Minghui Shen, Wenzhi Lv, and Zhengjia He. "Multi-Fault Classifier Based on Support Vector Machine and Its Application." In Damage Assessment of Structures VI, 483–92. Stafa: Trans Tech Publications Ltd., 2005. http://dx.doi.org/10.4028/0-87849-976-8.483.
Full textConference papers on the topic "Structured Support Vector Machine"
Sungwoong Kim, Jongmin Kim, Sungrack Yun, and Chang D. Yoo. "υ-structured support vector machines." In 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2010. http://dx.doi.org/10.1109/mlsp.2010.5588703.
Full textRangkuti, Rizki Perdana, Aprinaldi Jasa Mantau, Vektor Dewanto, Novian Habibie, and Wisnu Jatmiko. "Structured support vector machine learning of conditional random fields." In 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE, 2016. http://dx.doi.org/10.1109/icacsis.2016.7872799.
Full textLi, Yunpeng, and Daniel P. Huttenlocher. "Learning for stereo vision using the structured support vector machine." In 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2008. http://dx.doi.org/10.1109/cvpr.2008.4587699.
Full textTsochantaridis, Ioannis, Thomas Hofmann, Thorsten Joachims, and Yasemin Altun. "Support vector machine learning for interdependent and structured output spaces." In Twenty-first international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1015330.1015341.
Full textZien, Alexander, Ulf Brefeld, and Tobias Scheffer. "Transductive support vector machines for structured variables." In the 24th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1273496.1273645.
Full textBasudhar, Anirban, and Samy Missoum. "Local Update of Support Vector Machine Decision Boundaries." In 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-2189.
Full textPei-Yi Hao. "A new fuzzy maximal-margin spherical-structured multi-class support vector machine." In 2013 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2013. http://dx.doi.org/10.1109/icmlc.2013.6890475.
Full textYang, J., R. C. van Dalen, S. X. Zhang, and M. J. F. Gales. "Infinite structured support vector machines for speech recognition." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854215.
Full textTang, Hao, Chao-Hong Meng, and Lin-Shan Lee. "An initial attempt for phoneme recognition using Structured Support Vector Machine (SVM)." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495097.
Full textLee, Hung-yi, Yu-yu Chou, Yow-Bang Wang, and Lin-shan Lee. "Unsupervised domain adaptation for spoken document summarization with structured support vector machine." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6639293.
Full textReports on the topic "Structured Support Vector Machine"
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, December 2020. http://dx.doi.org/10.22617/wps200434-2.
Full textTabata, 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.
Full textGertz, 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.
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