Semi-supervised learning combines both labeled and unlabeled examples in order to find better future predictions. Semi-supervised support vector machines (SSSVM) present a non-convex optimization problem. In this paper a genetic algorithm is used to optimize the non-convex error-GSSSVM. It is experimented with multiple datasets and the performance of the genetic algorithm is compared to its supervised equivalent and shows very good results. A tailor-made modification of the genetic algorithm is also proposed which uses less unlabeled examples-the closest neighbors of the labeled instances.
CITATION STYLE
Lazarova, G. (2016). Semi-supervised support vector machines - A genetic algorithm approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9948 LNCS, pp. 241–249). Springer Verlag. https://doi.org/10.1007/978-3-319-46672-9_28
Mendeley helps you to discover research relevant for your work.