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A randomized algorithm for the exact solution of transductive support vector machines

Autor
Esposito, G.; Martin, M.
Tipus d'activitat
Article en revista
Revista
Applied artificial intelligence
Data de publicació
2015-05-13
Volum
29
Número
5
Pàgina inicial
459
Pàgina final
471
DOI
https://doi.org/10.1080/08839514.2015.1035951 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/83017 Obrir en finestra nova
URL
http://www.tandfonline.com/doi/abs/10.1080/08839514.2015.1035951?journalCode=uaai20 Obrir en finestra nova
Resum
Random sampling is an efficient method for dealing with constrained optimization problems. In computational geometry, this method has been successfully applied, through Clarkson’s algorithm (Clarkson 1996), to solve a general class of problems called violator spaces. In machine learning, Transductive Support Vector Machines (TSVM) is a learning method used when only a small fraction of labeled data is available, which implies solving a nonconvex optimization problem. Several approximation meth...
Citació
Esposito, G., Martin, M. A randomized algorithm for the exact solution of transductive support vector machines. "Applied artificial intelligence", 13 Maig 2015, vol. 29, núm. 5, p. 459-471.
Paraules clau
Classification, Semisupervised Learning, Transduction Support Vector Machine
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
KEMLG - Grup d´Enginyeria del Coneixement i Aprenentatge Automàtic

Participants

Arxius