F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation

Preprint English OPEN
Wu, Xiaohe; Zuo, Wangmeng; Zhu, Yuanyuan; Lin, Liang;
(2015)
  • Subject: Computer Science - Computer Vision and Pattern Recognition | Computer Science - Learning
    arxiv: Computer Science::Machine Learning | Computer Science::Computer Vision and Pattern Recognition | Computer Science::Sound | Statistics::Machine Learning
    acm: ComputingMethodologies_PATTERNRECOGNITION

The generalization error bound of support vector machine (SVM) depends on the ratio of radius and margin, while standard SVM only considers the maximization of the margin but ignores the minimization of the radius. Several approaches have been proposed to integrate radi... View more
  • References (51)
    51 references, page 1 of 6

    [1] V. Vapnik, Statistical learning theory. Wiley New York, 1998, vol. 1.

    [2] N. Cristianini and J. Shawe-Taylor, An introduction to support vector machines and other kernel-based learning methods. Cambridge university press, 2000.

    [3] E. Osuna, R. Freund, and F. Girosi, “Training support vector machines: an application to face detection,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1997, pp. 130-136.

    [4] B. Heisele, P. Ho, and T. Poggio, “Face recognition with support vector machines: Global versus component-based approach,” in IEEE International Conference on Computer Vision, vol. 2, 2001, pp. 688- 694.

    [5] G. Guo, S. Z. Li, and K. L. Chan, “Support vector machines for face recognition,” Image and Vision computing, vol. 19, no. 9, pp. 631-638, 2001.

    [6] J. Wu and H. Yang, “Linear regression-based efficient svm learning for large-scale classification.” IEEE transactions on neural networks and learning systems, 2015.

    [7] B. Samanta, K. Al-Balushi, and S. Al-Araimi, “Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection,” Engineering Applications of Artificial Intelligence, vol. 16, no. 7, pp. 657-665, 2003.

    [8] S. Chen, A. K. Samingan, and L. Hanzo, “Support vector machine multiuser receiver for ds-cdma signals in multipath channels,” Neural Networks, vol. 12, no. 3, pp. 604-611, 2001.

    [9] R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, “Natural language processing (almost) from scratch,” The Journal of Machine Learning Research, vol. 12, pp. 2493-2537, 2011.

    [10] I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun, “Support vector machine learning for interdependent and structured output spaces,” in Proc. of international conference on Machine learning, 2004, p. 104.

  • Metrics
    No metrics available
Share - Bookmark