Robust Unsupervised and Semi-supervised Bounded ν - Support Vector Machines
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Unsupervised and semi-supervised Lagrangian support vector machines with polyhedral perturbations
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
| Hi-index | 0.00 |
Support VectorMachines have been a dominant learning technique for almost ten years, moreover they have been applied to supervised learning problems. Recently twoclass unsupervised and semi-supervised classification problems based on Bounded C-Support Vector Machines are relaxed to semi-definite programming[7]. In this paper we will present another version to two-class unsupervised and semi-supervised classification problems based on Bounded í-Support Vector Machines, which trained by convex relaxation of the training criterion: find a labeling that yield a maximum margin on the training data. But the problems have difficulty to compute, we will find their semidefinite relaxations that can approximate them well. Experimental results show that our new unsupervised and semisupervised classification algorithms often obtain more accurate results than other unsupervised and semi-supervised methods.