Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Classifier fusion with interval-valued weights
Pattern Recognition Letters
Clustering-based ensembles for one-class classification
Information Sciences: an International Journal
Hi-index | 0.00 |
This paper presents an extension of the one-class support vector machines (OC-SVM) into an ensemble of soft OC-SVM classifiers. The idea consists in prior clustering of the input data with a kernel version of the deterministically annealed fuzzy c-means. This way partitioned data is trained with a number of soft OC-SVM classifiers which allow weight assignment to each of the training data. Weights are obtained from the cluster membership values, computed in the kernel fuzzy c-means. The method was designed and tested mostly in the tasks of image classification and segmentation, although it can be used for other one-class problems.