Title :
Informative Vector Machines for Text Categorization
Author :
Milos Stankovic;Srdan Stankovic
Author_Institution :
IRITEL, Belgrade
Abstract :
In this paper an analysis is given of the application of Bayesian Gaussian process statistical learning algorithms to the problem of text categorization. It is demonstrated that the informative vector machine method, as a sparse Bayesian compression scheme, provides results better than those obtained so far with the support vector machine method, with much less computational cost
Keywords :
"Text categorization","Support vector machines","Support vector machine classification","Machine learning","Bayesian methods","Gaussian processes","Machine learning algorithms","Neural networks","Learning systems","Seminars"
Conference_Titel :
Neural Network Applications in Electrical Engineering, 2006. NEUREL 2006. 8th Seminar on
Print_ISBN :
1-4244-0432-0
DOI :
10.1109/NEUREL.2006.341186
