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http://cseweb.ucsd.edu/~akmenon/ResearchExam.pdf
Large-Scale Support Vector Machines: Algorithms and Theory Aditya Krishna Menon ABSTRACT Support vector machines (SVMs) are a very popular method for binary classification. Traditional training algorithms for SVMs, such as chunking and SMO, scale superlinearly with the number of examples, which quickly becomes infeasible for large training sets.Cited by: 57
http://www.utc.fr/~bordesan/dokuwiki/_media/en/bordes10phdthesis-slides.pdf
New Algorithms for Large-Scale Support Vector Machines PhD Thesis Defense Thesis advisor: Patrick Gallinari Antoine Bordes LIP6 - Université Pierre et Marie Curie - DGA
http://cseweb.ucsd.edu/~akmenon/ResearchExamTalk.pdf
Outline 1 Introduction: large-scale learning 2 Background: Support vector machines 3 Primal training methods Stochastic gradient methods Pegasos 4 Dual training methods Dual coordinate descent Bundle method (BMRM) 5 Stochastic gradient in learning 6 Summary and future directions 7 References Aditya Menon (UCSD) Large-Scale Support Vector Machines: Algorithms and TheoryFebruary 27, 2009 2 / 73Cited by: 57
https://tel.archives-ouvertes.fr/tel-00464007/document
New Algorithms for Large-Scale Support Vector Machines Nouveaux Algorithmes pour l’Apprentissage de Machines `a Vecteurs Supports sur de Grandes Masses de Donn´ees soutenue publiquement le 9 f´evrier 2010 devant le jury compos´e de Jacques Blanc-Talon Responsable scientifique de l’ing´enierie de l’information `a la DGA ExaminateurCited by: 8
https://www.hds.utc.fr/~bordesan/dokuwiki/_media/en/bordes10phdthesis.pdf
New Algorithms for Large-Scale Support Vector Machines Nouveaux Algorithmes pour l’Apprentissage de Machines a Vecteurs Supports sur de Grandes Masses de Donn ees soutenue publiquement le 9 f evrier 2010 devant le jury compos e de Jacques Blanc-Talon Responsable scienti que de l’ing enierie de l’information a la DGA Examinateur
https://en.wikipedia.org/wiki/Support-vector_machine
In machine learning, support-vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category ...
http://www.stat.purdue.edu/~yuzhu/stat598m3/Papers/NewSVM.pdf
New Support Vector Algorithms 1209 Figure 1: In SV regression, a desired accuracyeis speci” ed a priori. It is then attempted to ” t a tube with radiuseto the data. The trade-off between model complexity andpoints lying outside the tube (withpositive slackvariablesj)is determined by minimizing the expression 1.5. subjectto ((w¢xi)Cb)¡yi ...
https://content.sciendo.com/view/journals/fcds/40/1/article-p67.xml
Two New Decomposition Algorithms for Training Bound-Constrained Support Vector Machines* ... Zanni L. Serafini T. Zanghirati G. Parallel software for training large scale support vector machines on multiprocessor systems Journal of Maching Learning Research 7 2006 1467-1492.
https://www.researchgate.net/publication/322838668_New_Incremental_Learning_Algorithm_With_Support_Vector_Machines
In addition, regression and classification algorithms like support vector machines [46] and artificial neural networks [47] can be applied to predict students' grades in the next examination or ...
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.71.5285&rep=rep1&type=pdf
This principle was further extended to Support Vector Machines (SVM) based experts by Kwok [3] and Rida, Labbi and Pellegrini [4]. The majority of the large-scale classiflcation meth-ods, however, are not appropriate for problems con-taining large numbers of classes, e.g. classifying thou-sands of categories. This kind of problem usually also
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