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Support Vector Machines: Training and Applications

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dc.contributor.author Osuna, Edgar en_US
dc.contributor.author Freund, Robert en_US
dc.contributor.author Girosi, Federico en_US
dc.date.accessioned 2004-10-22T20:17:54Z
dc.date.available 2004-10-22T20:17:54Z
dc.date.issued 1997-03-01 en_US
dc.identifier.other AIM-1602 en_US
dc.identifier.other CBCL-144 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/7290
dc.description.abstract The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images. en_US
dc.format.extent 38 p. en_US
dc.format.extent 6171554 bytes
dc.format.extent 2896170 bytes
dc.format.mimetype application/postscript
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartofseries AIM-1602 en_US
dc.relation.ispartofseries CBCL-144 en_US
dc.subject AI en_US
dc.subject MIT en_US
dc.subject Artificial Intelligence en_US
dc.subject Patter recognition en_US
dc.subject Support Vector Machine en_US
dc.subject Classification en_US
dc.subject Detection en_US
dc.title Support Vector Machines: Training and Applications en_US


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