Corrected Stochastic Dual Coordinate Ascent for Top-k SVM

Author:

HIROHASHI Yoshihiro1,KATO Tsuyoshi23

Affiliation:

1. DENSO CORPORATION

2. Graduate School of Science and Technology, Gunma University

3. Center for Research on Adoption of NextGen Transportation Systems, Gunma University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Reference29 articles.

1. [1] L. Berrada, A. Zisserman, and M.P. Kumar, “Smooth loss functions for deep top-k classification,” 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30-May 3, 2018, Conference Track Proceedings, 2018.

2. [2] D.P. Bertsekas, Nonlinear Programming, Athena Scientific, 1999.

3. [3] X. Chang, Y.-L. Yu, and Y. Yang, “Robust top-k multiclass SVM for visual category recognition,” Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining-KDD 17, ACM Press, pp.75-83, 2017. 10.1145/3097983.3097991

4. [4] D. Chu, R. Lu, J. Li, X. Yu, C. Zhang, and Q. Tao, “Optimizing top-k multiclass SVM via semismooth newton algorithm,” IEEE Transactions on Neural Networks and Learning Systems, vol.29, no.12, pp.6264-6275, Dec. 2018. 10.1109/tnnls.2018.2826039

5. [5] K. Crammer and Y. Singer, “On the algorithmic implementation of multiclass kernel-based vector machines,” J. Mach. Learn. Res., vol.2, pp.265-292, March 2002.

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