Show simple item record

dc.contributor.authorOsuna, Edgaren_US
dc.contributor.authorFreund, Roberten_US
dc.contributor.authorGirosi, Federicoen_US
dc.date.accessioned2004-10-22T20:17:54Z
dc.date.available2004-10-22T20:17:54Z
dc.date.issued1997-03-01en_US
dc.identifier.otherAIM-1602en_US
dc.identifier.otherCBCL-144en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/7290
dc.description.abstractThe 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.extent38 p.en_US
dc.format.extent6171554 bytes
dc.format.extent2896170 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesAIM-1602en_US
dc.relation.ispartofseriesCBCL-144en_US
dc.subjectAIen_US
dc.subjectMITen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectPatter recognitionen_US
dc.subjectSupport Vector Machineen_US
dc.subjectClassificationen_US
dc.subjectDetectionen_US
dc.titleSupport Vector Machines: Training and Applicationsen_US


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record