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Artykuły w czasopismach na temat "SVM"
Wang, Bo, Yu Kai Yao, Xiao Ping Wang i Xiao Yun Chen. "PB-SVM Ensemble: A SVM Ensemble Algorithm Based on SVM". Applied Mechanics and Materials 701-702 (grudzień 2014): 58–62. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.58.
Pełny tekst źródłaZHU, Yongsheng. "A new type SVM??projected SVM". Science in China Series G 47, nr 7 (2004): 21. http://dx.doi.org/10.1360/03yb0244.
Pełny tekst źródłaHuang, Wencheng, Hongyi Liu, Yue Zhang, Rongwei Mi, Chuangui Tong, Wei Xiao i Bin Shuai. "Railway dangerous goods transportation system risk identification: Comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM". Applied Soft Computing 109 (wrzesień 2021): 107541. http://dx.doi.org/10.1016/j.asoc.2021.107541.
Pełny tekst źródłaYanling, Xu, Wu Baolin i Baolin Liushan. "A Network-Adapative SVC Streaming Strategy with SVM-Based Bandwidth Prediction". International Journal of Future Computer and Communication 3, nr 3 (2014): 205–9. http://dx.doi.org/10.7763/ijfcc.2014.v3.297.
Pełny tekst źródłaSHIMADA, KAZUTAKA, KOJI HAYASHI i TSUTOMU ENDO. "Product Specification Extraction Using SVM and Transductive SVM". Journal of Natural Language Processing 12, nr 3 (2005): 43–66. http://dx.doi.org/10.5715/jnlp.12.3_43.
Pełny tekst źródłaHuang, Min-Wei, Chih-Wen Chen, Wei-Chao Lin, Shih-Wen Ke i Chih-Fong Tsai. "SVM and SVM Ensembles in Breast Cancer Prediction". PLOS ONE 12, nr 1 (6.01.2017): e0161501. http://dx.doi.org/10.1371/journal.pone.0161501.
Pełny tekst źródłaLapin, Maksim, Matthias Hein i Bernt Schiele. "Learning using privileged information: SVM+ and weighted SVM". Neural Networks 53 (maj 2014): 95–108. http://dx.doi.org/10.1016/j.neunet.2014.02.002.
Pełny tekst źródłaSafiya, K. M. "Genetic Algorithm with SRM SVM Classifier for Face Verification". International Journal of Computer Science and Information Technology 4, nr 4 (31.08.2012): 151–63. http://dx.doi.org/10.5121/ijcsit.2012.4414.
Pełny tekst źródłaDeepthi, Medechal, Mosali Harini, Pandiri Sai Geethika, Vusirikala Kalyan i K. Kishor. "Data Classification of Dark Web using SVM and S3VM". International Journal for Research in Applied Science and Engineering Technology 11, nr 9 (30.09.2023): 510–17. http://dx.doi.org/10.22214/ijraset.2023.55643.
Pełny tekst źródłaArdjani, Fatima, i Kaddour Sadouni. "Optimization of SVM Multiclass by Particle Swarm (PSO-SVM)". International Journal of Modern Education and Computer Science 2, nr 2 (16.12.2010): 32–38. http://dx.doi.org/10.5815/ijmecs.2010.02.05.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaleurs 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.
Pełny tekst źródłaSynek, 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.
Pełny tekst źródłaMELONI, 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.
Pełny tekst źródłaClassificação 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.
Pełny tekst źródłaIncludes 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.
Pełny tekst źródłaYao, 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.
Pełny tekst źródłaSýkora, Michal. "Automatické označování obrázků". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236453.
Pełny tekst źródłaxu, 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.
Pełny tekst źródłaOntologies 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.
Książki na temat "SVM"
S, Triandofilidi R., i Aĭvazi͡a︡n Sergeĭ Artemʹevich, red. Rukovodstvo po operat͡s︡ionnoĭ sisteme SVM ES. Moskva: T͡S︡entr. ėkonomiko-matematicheskiĭ in-t Akademii nauk SSSR, 1988.
Znajdź pełny tekst źródłaSvM: Die Festschrift : für Stanislaus von Moos. Zürich: gta Verlag, 2005.
Znajdź pełny tekst źródłaAlekseev, A. V. Programmirovanie v podsisteme dialogovoĭ obrabotki SVM ES: A.V. Alekseev, D.D. Gorbatenko, A.V. Serzhantov. Moskva: "Radio i svi͡a︡zʹ", 1990.
Znajdź pełny tekst źródła-W, Lee S., i Verri Alessandro, red. Pattern recognition with support vector machines: First international workshop, SVM 2002, Niagara Falls, Canada, August 202 : proceedings. Berlin: Springer, 2002.
Znajdź pełny tekst źródłaGermany), 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.
Znajdź pełny tekst źródłaGermany), 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.
Znajdź pełny tekst źródłaOthmar, Marti, i Amrein Matthias, red. STM and SFM in biology. San Diego: Academic Press, 1993.
Znajdź pełny tekst źródłaArko, Andraž, i Jan Dominik Bogataj. Sem mislil da sem sam: Hagiografska drama o mučencu Lojzetu Grozdetu. Ljubljana: Založba Brat Frančišek, 2012.
Znajdź pełny tekst źródłaFisher, Roger. Como chegar ao sim: Negociação de acordos sem concessões. Wyd. 2. Rio de Janeiro: Imago, 2005.
Znajdź pełny tekst źródłaKrižanec, Siniša. Svi bute me tužili, ili, "Kako sam pomirio duhove". Zagreb: ITD d.o.o, 1999.
Znajdź pełny tekst źródłaCzęści książek na temat "SVM"
Beney, Jean, i Cornelis H. A. Koster. "SVM Paradoxes". W Perspectives of Systems Informatics, 86–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11486-1_8.
Pełny tekst źródłaYang, Yuli, Zhi Li i Yanfeng Wang. "Risk Prediction of Esophageal Cancer Using SOM Clustering, SVM and GA-SVM". W Communications in Computer and Information Science, 345–58. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3415-7_29.
Pełny tekst źródłaParand, Kourosh, Fatemeh Baharifard, Alireza Afzal Aghaei i Mostafa Jani. "Basics of SVM Method and Least Squares SVM". W 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.
Pełny tekst źródłaWu, Chao-Chin, De-Xang Wang i Lien-Fu Lai. "Accelerate SVM Training with OHD-SVM on GPU". W Big Data – BigData 2023, 209–17. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44725-9_15.
Pełny tekst źródłaMurty, M. N., i Rashmi Raghava. "Kernel-Based SVM". W Support Vector Machines and Perceptrons, 57–67. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41063-0_5.
Pełny tekst źródłaMatías, José M. "Partially Parametric SVM". W Progress in Artificial Intelligence, 67–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11595014_7.
Pełny tekst źródłaVeisi, Hadi. "Introduction to SVM". W 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.
Pełny tekst źródłaYu, Hwanjo, Youngdae Kim i Seungwon Hwang. "RV-SVM: An Efficient Method for Learning Ranking SVM". W 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.
Pełny tekst źródłaDas, Banee Bandana, Saswat Kumar Ram, Bibudhendu Pati, Chhabi Rani Panigrahi, Korra Sathya Babu i Ramesh Kumar Mohapatra. "SVM and Ensemble-SVM in EEG-Based Person Identification". W Advances in Intelligent Systems and Computing, 137–46. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6353-9_13.
Pełny tekst źródłaVerma, Praveen, Tushar Bhardwaj, Abhay Bhatia i Mohd Mursleen. "Sentiment Analysis “Using SVM, KNN and SVM with PCA”". W 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.
Pełny tekst źródłaStreszczenia konferencji na temat "SVM"
Fink, Eric, Jaroslaw Kwapisz i Ioannis Roudas. "Optimized SVM constellations for SDM fibers". W 2021 IEEE Photonics Conference (IPC). IEEE, 2021. http://dx.doi.org/10.1109/ipc48725.2021.9593013.
Pełny tekst źródłaSun, Mingshun, Yanmao Man, Ming Li i Ryan Gerdes. "SVM". W 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.
Pełny tekst źródłaLudwig, Oswaldo, Cristiano Premebida, Urbano Nunes i Rui Araujo. "Evaluation of Boosting-SVM and SRM-SVM cascade classifiers in laser and vision-based pedestrian detection". W 2011 14th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/itsc.2011.6082909.
Pełny tekst źródłaCai, Hong, i Yufeng Wang. "Transcriptomic analysis using SVD clustering and SVM classification". W 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2011. http://dx.doi.org/10.1109/gensips.2011.6169476.
Pełny tekst źródłaLi, Zhongguo, Jie Hou, Qi Wang i Qinghua Liu. "Road type recognition based on SOM and SVM". W 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet). IEEE, 2011. http://dx.doi.org/10.1109/cecnet.2011.5768757.
Pełny tekst źródłaFan, Yan-Feng, De-Xian Zhang i Hua-Can He. "Tangent Circular Arc Smooth SVM (TCA-SSVM) Research". W 2008 Congress on Image and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/cisp.2008.112.
Pełny tekst źródłaShalev-Shwartz, Shai, i Nathan Srebro. "SVM optimization". W the 25th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390273.
Pełny tekst źródłaStanley-Marbell, Phillip. "Sal/Svm". W Virtual Machines and Intermediate Languages. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1941054.1941055.
Pełny tekst źródłaChang, Qingqing, Shaofu Lin i Xiliang Liu. "Stacked-SVM". W 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.
Pełny tekst źródła"VISUAL SVM". W 7th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0002521003090314.
Pełny tekst źródłaRaporty organizacyjne na temat "SVM"
Davenport, Mark A. The 2nu-SVM: A Cost-Sensitive Extension of the nu-SVM. Fort Belvoir, VA: Defense Technical Information Center, grudzień 2005. http://dx.doi.org/10.21236/ada486719.
Pełny tekst źródłaCarin, Lawrence. ICA Feature Extraction and SVM Classification of FLIR Imagery. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 2005. http://dx.doi.org/10.21236/ada441506.
Pełny tekst źródłaMorris, Brendan, David W. Aha, Bryan Auslander i Kalyan Gupta. Learning and Leveraging Context for Maritime Threat Analysis: Vessel Classification using Exemplar-SVM. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 2012. http://dx.doi.org/10.21236/ada574666.
Pełny tekst źródłaLi, Qi. Application of Improved Feature Selection Algorithm in SVM Based Market Trend Prediction Model. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.6614.
Pełny tekst źródłaKarypis, George. Better Kernels and Coding Schemes Lead to Improvements in SVM-Based Secondary Structure Prediction. Fort Belvoir, VA: Defense Technical Information Center, lipiec 2005. http://dx.doi.org/10.21236/ada439626.
Pełny tekst źródłaNaseem, Shahid. Hand written digits classification and recognition using convolutional neural networks by implementing the techniques of MLP and SVM. Peeref, marzec 2023. http://dx.doi.org/10.54985/peeref.2303p8226220.
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.
Pełny tekst źródłaBrownlee, N. SVG Drawings for RFCs: SVG 1.2 RFC. RFC Editor, grudzień 2016. http://dx.doi.org/10.17487/rfc7996.
Pełny tekst źródłaLing, Alice V., András Vládar, Bradley N. Damazo, M. Alkan Donmez i Michael T. Postek. SEM Sentinel:. Gaithersburg, MD: National Institute of Standards and Technology, 2000. http://dx.doi.org/10.6028/nist.ir.6498.
Pełny tekst źródłaZyphur, Michael. Intermediate SEM in Stata: From CFA to SEM. Instats Inc., 2022. http://dx.doi.org/10.61700/9qo0ssbbzp4nl469.
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