preprint · arXiv (Cornell University) · 2016
One-class slab support vector machine
Victor Fragoso, Walter J. Scheirer, João P. Hespanha, Matthew Turk
Cited by 1Open access
Abstract
This work introduces the one-class slab SVM (OCSSVM), a one-class classifier that aims at improving the performance of the one-class SVM. The proposed strategy reduces the false positive rate and increases the accuracy of detecting instances from novel classes. To this end, it uses two parallel hyperplanes to learn the normal region of the decision scores of the target class. OCSSVM extends one-class SVM since it can scale and learn non-linear decision functions via kernel methods. The experiments on two publicly available datasets show that OCSSVM can consistently outperform the one-class SVM and perform comparable to or better than other state-of-the-art one-class classifiers.
Cite this paper
Fragoso, V., Scheirer, W. J., Hespanha, J. P., & Turk, M. (2016). One-class slab support vector machine. arXiv (Cornell University), 420–425. https://doi.org/10.1109/icpr.2016.7899670
Fragoso, Victor, et al. “One-class slab support vector machine.” arXiv (Cornell University), 2016, pp. 420–25, https://doi.org/10.1109/icpr.2016.7899670.
Fragoso, Victor, Walter J. Scheirer, João P. Hespanha, and Matthew Turk. 2016. “One-class slab support vector machine.” arXiv (Cornell University): 420–25. https://doi.org/10.1109/icpr.2016.7899670.
Fragoso, V. et al. (2016) ‘One-class slab support vector machine’, arXiv (Cornell University), pp. 420–425. Available at: https://doi.org/10.1109/icpr.2016.7899670.
@misc{fragoso2016class,
title = {{One-class slab support vector machine}},
author = {Fragoso, Victor and Scheirer, Walter J. and Hespanha, João P. and Turk, Matthew},
journal = {arXiv (Cornell University)},
pages = {420--425},
year = {2016},
doi = {10.1109/icpr.2016.7899670}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.