Academic literature on the topic 'SVM'
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Journal articles on the topic "SVM"
Wang, Bo, Yu Kai Yao, Xiao Ping Wang, and Xiao Yun Chen. "PB-SVM Ensemble: A SVM Ensemble Algorithm Based on SVM." Applied Mechanics and Materials 701-702 (December 2014): 58–62. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.58.
Full textZHU, Yongsheng. "A new type SVM??projected SVM." Science in China Series G 47, no. 7 (2004): 21. http://dx.doi.org/10.1360/03yb0244.
Full textHuang, Wencheng, Hongyi Liu, Yue Zhang, Rongwei Mi, Chuangui Tong, Wei Xiao, and Bin Shuai. "Railway dangerous goods transportation system risk identification: Comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM." Applied Soft Computing 109 (September 2021): 107541. http://dx.doi.org/10.1016/j.asoc.2021.107541.
Full textYanling, Xu, Wu Baolin, and Baolin Liushan. "A Network-Adapative SVC Streaming Strategy with SVM-Based Bandwidth Prediction." International Journal of Future Computer and Communication 3, no. 3 (2014): 205–9. http://dx.doi.org/10.7763/ijfcc.2014.v3.297.
Full textSHIMADA, KAZUTAKA, KOJI HAYASHI, and TSUTOMU ENDO. "Product Specification Extraction Using SVM and Transductive SVM." Journal of Natural Language Processing 12, no. 3 (2005): 43–66. http://dx.doi.org/10.5715/jnlp.12.3_43.
Full textHuang, Min-Wei, Chih-Wen Chen, Wei-Chao Lin, Shih-Wen Ke, and Chih-Fong Tsai. "SVM and SVM Ensembles in Breast Cancer Prediction." PLOS ONE 12, no. 1 (January 6, 2017): e0161501. http://dx.doi.org/10.1371/journal.pone.0161501.
Full textLapin, Maksim, Matthias Hein, and Bernt Schiele. "Learning using privileged information: SVM+ and weighted SVM." Neural Networks 53 (May 2014): 95–108. http://dx.doi.org/10.1016/j.neunet.2014.02.002.
Full textSafiya, K. M. "Genetic Algorithm with SRM SVM Classifier for Face Verification." International Journal of Computer Science and Information Technology 4, no. 4 (August 31, 2012): 151–63. http://dx.doi.org/10.5121/ijcsit.2012.4414.
Full textDeepthi, Medechal, Mosali Harini, Pandiri Sai Geethika, Vusirikala Kalyan, and K. Kishor. "Data Classification of Dark Web using SVM and S3VM." International Journal for Research in Applied Science and Engineering Technology 11, no. 9 (September 30, 2023): 510–17. http://dx.doi.org/10.22214/ijraset.2023.55643.
Full textArdjani, Fatima, and Kaddour Sadouni. "Optimization of SVM Multiclass by Particle Swarm (PSO-SVM)." International Journal of Modern Education and Computer Science 2, no. 2 (December 16, 2010): 32–38. http://dx.doi.org/10.5815/ijmecs.2010.02.05.
Full textDissertations / Theses on the topic "SVM"
Guermeur, Yann. "SVM Multiclasses, Théorie et Applications." Habilitation à diriger des recherches, Université Henri Poincaré - Nancy I, 2007. http://tel.archives-ouvertes.fr/tel-00203086.
Full textleurs performances constituent l'état de l'art dans de multiples domaines
de la reconnaissance des formes, d'autre part, elles possèdent des propriétés statistiques remarquables. Le premier modèle de SVM proposé par Vapnik et ses co-auteurs calcule des dichotomies. Il peut être utilisé pour effectuer des tâches de discrimination à catégories multiples, dans le cadre de l'application de méthodes de décomposition. Des SVM multi-classes ont également été proposées dans la littérature, parmi lesquelles nous distinguons celles qui s'appuient sur un modèle affine multivarié, que nous nommons M-SVM. Ce mémoire se présente comme une étude synthétique de la discrimination à catégories multiples au moyen de SVM. Il se concentre plus particulièrement sur l'analyse des M-SVM.
Le chapitre deux est consacré à la description des SVM multi-classes,
à leur mise en oeuvre et à l'analyse de leurs performances. Nous présentons successivement le cadre théorique de leur étude, les différents modèles, une étude théorique de leurs performances en généralisation, leur programmation ainsi que les différentes méthodes de sélection de modèle qui leur sont dédiées. Le chapitre trois décrit une application de la M-SVM de Weston et Watkins en biologie structurale prédictive. Le problème traité est la prédiction de la structure secondaire des protéines globulaires.
Krontorád, Jan. "Implementace algoritmu SVM v FPGA." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236773.
Full textSynek, Radovan. "Klasifikace textu pomocí metody SVM." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237229.
Full textMELONI, RAPHAEL BELO DA SILVA. "REMOTE SENSING IMAGE CLASSIFICATION USING SVM." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=31439@1.
Full textClassificação de imagens é o processo de extração de informação em imagens digitais para reconhecimento de padrões e objetos homogêneos, que em sensoriamento remoto propõe-se a encontrar padrões entre os pixels pertencentes a uma imagem digital e áreas da superfície terrestre, para uma análise posterior por um especialista. Nesta dissertação, utilizamos a metodologia de aprendizado de máquina support vector machines para o problema de classificação de imagens, devido a possibilidade de trabalhar com grande quantidades de características. Construímos classificadores para o problema, utilizando imagens distintas que contém as informações de espaços de cores RGB e HSB, dos valores altimétricos e do canal infravermelho de uma região. Os valores de relevo ou altimétricos contribuíram de forma excelente nos resultados, uma vez que esses valores são características fundamentais de uma região e os mesmos não tinham sido analisados em classificação de imagens de sensoriamento remoto. Destacamos o resultado final, do problema de classificação de imagens, para o problema de identificação de piscinas com vizinhança dois. Os resultados obtidos são 99 por cento de acurácia, 100 por cento de precisão, 93,75 por cento de recall, 96,77 por cento de F-Score e 96,18 por cento de índice Kappa.
Image Classification is an information extraction process in digital images for pattern and homogeneous objects recognition. In remote sensing it aims to find patterns from digital images pixels, covering an area of earth surface, for subsequent analysis by a specialist. In this dissertation, to this images classification problem we employ Support Vector Machines, a machine learning methodology, due the possibility of working with large quantities of features. We built classifiers to the problem using different image information, such as RGB and HSB color spaces, altimetric values and infrared channel of a region. The altimetric values contributed to excellent results, since these values are fundamental characteristics of a region and they were not previously considered in remote sensing images classification. We highlight the final result, for the identifying swimming pools problem, when neighborhood is two. The results have 99 percent accuracy, 100 percent precision, 93.75 percent of recall, 96.77 percent F-Score and 96.18 percent of Kappa index.
Chen, Kathy F. (Kathy Fang-Yun). "Offline and online SVM performance analysis." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41259.
Full textIncludes bibliographical references (p. 51-52).
To understand and evaluate the performance of a machine learning algorithm, the Support Vector Machine, this thesis compares the strengths and weaknesses between the offline and online SVM. The work includes the performance comparisons of SVMLight and LaSVM, with results of training time, number of support vectors, kernel evaluations, and test accuracies. Multiple datasets are experimented to cover a wide range of input data and training problems. Overall, the online LaSVM has trained with less time and returned comparable test accuracies than SVMLight. A general breakdown of the two algorithms and their computation efforts are included for detailed analysis.
by Kathy F. Chen.
M.Eng.
TEIXEIRA, JÚNIOR Talisman Cláudio de Queiroz. "Classificação fonética utilizando Boosting e SVM." Universidade Federal do Pará, 2006. http://repositorio.ufpa.br/jspui/2011/2533.
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Para compor um sistema de Reconhecimento Automático de Voz, pode ser utilizada uma tarefa chamada Classificação Fonética, onde a partir de uma amostra de voz decide-se qual fonema foi emitido por um interlocutor. Para facilitar a classificação e realçar as características mais marcantes dos fonemas, normalmente, as amostras de voz são pré- processadas através de um fronl-en'L Um fron:-end, geralmente, extrai um conjunto de parâmetros para cada amostra de voz. Após este processamento, estes parâmetros são insendos em um algoritmo classificador que (já devidamente treinado) procurará decidir qual o fonema emitido. Existe uma tendência de que quanto maior a quantidade de parâmetros utilizados no sistema, melhor será a taxa de acertos na classificação. A contrapartida para esta tendência é o maior custo computacional envolvido. A técnica de Seleção de Parâmetros tem como função mostrar quais os parâmetros mais relevantes (ou mais utilizados) em uma tarefa de classificação, possibilitando, assim, descobrir quais os parâmetros redundantes, que trazem pouca (ou nenhuma) contribuição à tarefa de classificação. A proposta deste trabalho é aplicar o classificador SVM à classificação fonética, utilizando a base de dados TIMIT, e descobrir os parâmetros mais relevantes na classificação, aplicando a técnica Boosting de Seleção de Parâmetros.
With the aim of setting up a Automatic Speech Recognition (ASR) system, a task named Phonetic Classification can be used. That task consists in, from a speech sample, deciding which phoneme was pronounced by a speaker. To ease the classification task and to enhance the most marked characteristics of the phonemes, the speech samples are usually pre-processed by a front-end. A front-end, as a general rule, extracts a set of features to each speech sample. After that, these features are inserted in a classification algorithm, that (already properly trained) will try to decide which phoneme was pronounced. There is a rule of thumb which says that the more features the system uses, the smaller the classification error rate will be. The disadvantage to that is the larger computational cost. Feature Selection task aims to show which are the most relevant (or more used) features in a classification task. Therefore, it is possible to discover which are the redundant features, that make little (or no) contribution to the classification task. The aim of this work is to apply SVM classificator in Phonetic Classification task, using TIMIT database, and discover the most relevant features in this classification using Boosting approach to implement Feature Selection.
Štechr, Vladislav. "Využití SVM v prostředí finančních trhů." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2016. http://www.nusl.cz/ntk/nusl-241651.
Full textYao, Xiaojun. "Méthodes Non-linéaires (ANNs, SVMs) : applications à la Classification et à la Corrélation des Propriétés Physicochimiques et Biologiques." Paris 7, 2004. http://www.theses.fr/2004PA077182.
Full textSýkora, Michal. "Automatické označování obrázků." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236453.
Full textxu, wei. "SVM-based algorithms for aligning ontologies using literature." Thesis, Linköping University, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15974.
Full textOntologies is one of the key techniques used in Semantic Web establishment. Nowadays,many ontologies have been developed and it is critical to understand the relationships between the terms of the ontologies, i.e. we need to align the ontologies.
This thesis deals with an approach for finding relationships between ontologies using literature by classifying documents related to terms in the ontologies.
In this project the general method from [1] is used, but in the classifier generation part, a brand new classifier based on SVMs algorithm is implemented by LPU and SVMlight. We evaluate our approach and compare it to previous approaches.
Books on the topic "SVM"
S, Triandofilidi R., and Aĭvazi͡a︡n Sergeĭ Artemʹevich, eds. Rukovodstvo po operat͡s︡ionnoĭ sisteme SVM ES. Moskva: T͡S︡entr. ėkonomiko-matematicheskiĭ in-t Akademii nauk SSSR, 1988.
Find full textSvM: Die Festschrift : für Stanislaus von Moos. Zürich: gta Verlag, 2005.
Find full textAlekseev, A. V. Programmirovanie v podsisteme dialogovoĭ obrabotki SVM ES: A.V. Alekseev, D.D. Gorbatenko, A.V. Serzhantov. Moskva: "Radio i svi͡a︡zʹ", 1990.
Find full text-W, Lee S., and Verri Alessandro, eds. Pattern recognition with support vector machines: First international workshop, SVM 2002, Niagara Falls, Canada, August 202 : proceedings. Berlin: Springer, 2002.
Find full textGermany), SVM (2008 Munich. Systems and virtualization management: Standards and new technologies : second international workshop, SVM 2008, Munich, Germany, October 21-22, 2008, proceedings. Berlin: Springer, 2008.
Find full textGermany), SVM (2008 Munich. Systems and virtualization management: Standards and new technologies : second international workshop, SVM 2008, Munich, Germany, October 21-22, 2008, proceedings. Berlin: Springer, 2008.
Find full textOthmar, Marti, and Amrein Matthias, eds. STM and SFM in biology. San Diego: Academic Press, 1993.
Find full textArko, Andraž, and Jan Dominik Bogataj. Sem mislil da sem sam: Hagiografska drama o mučencu Lojzetu Grozdetu. Ljubljana: Založba Brat Frančišek, 2012.
Find full textFisher, Roger. Como chegar ao sim: Negociação de acordos sem concessões. 2nd ed. Rio de Janeiro: Imago, 2005.
Find full textKrižanec, Siniša. Svi bute me tužili, ili, "Kako sam pomirio duhove". Zagreb: ITD d.o.o, 1999.
Find full textBook chapters on the topic "SVM"
Beney, Jean, and Cornelis H. A. Koster. "SVM Paradoxes." In Perspectives of Systems Informatics, 86–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11486-1_8.
Full textYang, Yuli, Zhi Li, and Yanfeng Wang. "Risk Prediction of Esophageal Cancer Using SOM Clustering, SVM and GA-SVM." In Communications in Computer and Information Science, 345–58. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3415-7_29.
Full textParand, Kourosh, Fatemeh Baharifard, Alireza Afzal Aghaei, and Mostafa Jani. "Basics of SVM Method and Least Squares SVM." In Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines, 19–36. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6553-1_2.
Full textWu, Chao-Chin, De-Xang Wang, and Lien-Fu Lai. "Accelerate SVM Training with OHD-SVM on GPU." In Big Data – BigData 2023, 209–17. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44725-9_15.
Full textMurty, M. N., and Rashmi Raghava. "Kernel-Based SVM." In Support Vector Machines and Perceptrons, 57–67. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41063-0_5.
Full textMatías, José M. "Partially Parametric SVM." In Progress in Artificial Intelligence, 67–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11595014_7.
Full textVeisi, Hadi. "Introduction to SVM." In Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines, 3–18. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6553-1_1.
Full textYu, Hwanjo, Youngdae Kim, and Seungwon Hwang. "RV-SVM: An Efficient Method for Learning Ranking SVM." In Advances in Knowledge Discovery and Data Mining, 426–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_39.
Full textDas, Banee Bandana, Saswat Kumar Ram, Bibudhendu Pati, Chhabi Rani Panigrahi, Korra Sathya Babu, and Ramesh Kumar Mohapatra. "SVM and Ensemble-SVM in EEG-Based Person Identification." In Advances in Intelligent Systems and Computing, 137–46. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6353-9_13.
Full textVerma, Praveen, Tushar Bhardwaj, Abhay Bhatia, and Mohd Mursleen. "Sentiment Analysis “Using SVM, KNN and SVM with PCA”." In Artificial Intelligence in Cyber Security: Theories and Applications, 35–53. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28581-3_5.
Full textConference papers on the topic "SVM"
Fink, Eric, Jaroslaw Kwapisz, and Ioannis Roudas. "Optimized SVM constellations for SDM fibers." In 2021 IEEE Photonics Conference (IPC). IEEE, 2021. http://dx.doi.org/10.1109/ipc48725.2021.9593013.
Full textSun, Mingshun, Yanmao Man, Ming Li, and Ryan Gerdes. "SVM." In WiSec '20: 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3395351.3399348.
Full textLudwig, Oswaldo, Cristiano Premebida, Urbano Nunes, and Rui Araujo. "Evaluation of Boosting-SVM and SRM-SVM cascade classifiers in laser and vision-based pedestrian detection." In 2011 14th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/itsc.2011.6082909.
Full textCai, Hong, and Yufeng Wang. "Transcriptomic analysis using SVD clustering and SVM classification." In 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2011. http://dx.doi.org/10.1109/gensips.2011.6169476.
Full textLi, Zhongguo, Jie Hou, Qi Wang, and Qinghua Liu. "Road type recognition based on SOM and SVM." In 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet). IEEE, 2011. http://dx.doi.org/10.1109/cecnet.2011.5768757.
Full textFan, Yan-Feng, De-Xian Zhang, and Hua-Can He. "Tangent Circular Arc Smooth SVM (TCA-SSVM) Research." In 2008 Congress on Image and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/cisp.2008.112.
Full textShalev-Shwartz, Shai, and Nathan Srebro. "SVM optimization." In the 25th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390273.
Full textStanley-Marbell, Phillip. "Sal/Svm." In Virtual Machines and Intermediate Languages. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1941054.1941055.
Full textChang, Qingqing, Shaofu Lin, and Xiliang Liu. "Stacked-SVM." In ACAI 2019: 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3377713.3377735.
Full text"VISUAL SVM." In 7th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0002521003090314.
Full textReports on the topic "SVM"
Davenport, Mark A. The 2nu-SVM: A Cost-Sensitive Extension of the nu-SVM. Fort Belvoir, VA: Defense Technical Information Center, December 2005. http://dx.doi.org/10.21236/ada486719.
Full textCarin, Lawrence. ICA Feature Extraction and SVM Classification of FLIR Imagery. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada441506.
Full textMorris, Brendan, David W. Aha, Bryan Auslander, and Kalyan Gupta. Learning and Leveraging Context for Maritime Threat Analysis: Vessel Classification using Exemplar-SVM. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada574666.
Full textLi, Qi. Application of Improved Feature Selection Algorithm in SVM Based Market Trend Prediction Model. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6614.
Full textKarypis, George. Better Kernels and Coding Schemes Lead to Improvements in SVM-Based Secondary Structure Prediction. Fort Belvoir, VA: Defense Technical Information Center, July 2005. http://dx.doi.org/10.21236/ada439626.
Full textNaseem, Shahid. Hand written digits classification and recognition using convolutional neural networks by implementing the techniques of MLP and SVM. Peeref, March 2023. http://dx.doi.org/10.54985/peeref.2303p8226220.
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.
Full textBrownlee, N. SVG Drawings for RFCs: SVG 1.2 RFC. RFC Editor, December 2016. http://dx.doi.org/10.17487/rfc7996.
Full textLing, Alice V., András Vládar, Bradley N. Damazo, M. Alkan Donmez, and Michael T. Postek. SEM Sentinel:. Gaithersburg, MD: National Institute of Standards and Technology, 2000. http://dx.doi.org/10.6028/nist.ir.6498.
Full textZyphur, Michael. Intermediate SEM in Stata: From CFA to SEM. Instats Inc., 2022. http://dx.doi.org/10.61700/9qo0ssbbzp4nl469.
Full text